Systems and methods for processing sensor data

ABSTRACT

Systems and methods for processing sensor data are provided. In some embodiments, systems and methods are provided for calibration of a continuous analyte sensor. In some embodiments, systems and methods are provided for classification of a level of noise on a sensor signal. In some embodiments, systems and methods are provided for determining a rate of change for analyte concentration based on a continuous sensor signal. In some embodiments, systems and methods for alerting or alarming a patient based on prediction of glucose concentration are provided.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.61/014,398 filed Dec. 17, 2007, which is incorporated by referenceherein in its entirety, and is hereby expressly made a part of thisspecification.

FIELD OF THE INVENTION

The present invention relates generally to systems and methods forprocessing data received from an analyte sensor, such as a glucosesensor.

BACKGROUND OF THE INVENTION

Diabetes mellitus is a disorder in which the pancreas cannot createsufficient insulin (Type I or insulin dependent) and/or in which insulinis not effective (Type 2 or non-insulin dependent). In the diabeticstate, the victim suffers from high blood sugar, which causes an arrayof physiological derangements (kidney failure, skin ulcers, or bleedinginto the vitreous of the eye) associated with the deterioration of smallblood vessels. A hypoglycemic reaction (low blood sugar) is induced byan inadvertent overdose of insulin, or after a normal dose of insulin orglucose-lowering agent accompanied by extraordinary exercise orinsufficient food intake.

Conventionally, a diabetic person carries a self-monitoring bloodglucose (SMBG) monitor, which typically requires uncomfortable fingerpricking methods. Due to the lack of comfort and convenience, a diabeticwill normally only measure his or her glucose levels two to four timesper day. Unfortunately, these time intervals are spread so far apartthat the diabetic will likely find out too late, sometimes incurringdangerous side effects, of a hyperglycemic or hypoglycemic condition. Infact, it is not only unlikely that a diabetic will take a timely SMBGvalue, but additionally the diabetic will not know if his blood glucosevalue is going up (higher) or down (lower) based on conventionalmethods.

Consequently, a variety of glucose sensors are being developed forcontinuously detecting and/or quantifying blood glucose values. Manyimplantable glucose sensors suffer from complications within the bodyand provide only short-term and less-than-accurate sensing of bloodglucose. Similarly, transdermal sensors have run into problems inaccurately sensing and reporting back glucose values continuously overextended periods of time. Some efforts have been made to obtain bloodglucose data from implantable devices and retrospectively determineblood glucose trends for analysis; however these efforts do not aid thediabetic in determining real-time blood glucose information. Someefforts have also been made to obtain blood glucose data fromtransdermal devices for prospective data analysis, however similarproblems have occurred.

SUMMARY OF THE INVENTION

In a first aspect, a method for calibrating an analyte sensor isprovided, the method comprising: receiving sensor data from an analytesensor, the sensor data comprising at least one sensor data point;receiving reference data from a reference analyte monitor, the referencedata comprising at least one reference analyte value; matching at leastone sensor data point with at least one reference analyte value to forma matched data pair; converting sensor data into at least one estimatedanalyte value, utilizing the matched data pair; and displaying theestimated analyte value within about 10 minutes of receiving thereference analyte value.

In an embodiment of the first aspect, the step of displaying theestimated analyte value is performed within about 5 minutes of receivingthe reference analyte value.

In an embodiment of the first aspect, the step of displaying theestimated analyte value is performed within about 1 minute of receivingthe reference analyte value.

In a second aspect, a method for calibrating glucose sensor data from acontinuous glucose sensor is provided, the method comprising: obtaininga reference glucose value; and matching the reference glucose value witha sensor glucose value without compensating for a time lag between thereference glucose value and the sensor glucose value such that a timestamp for the reference glucose value is as close as possible to a timestamp of the sensor glucose value.

In an embodiment of the second aspect, the time stamp of the referenceglucose value is within about 5 minutes of the time stamp of the sensorglucose value.

In an embodiment of the second aspect, the time lag between thereference glucose value and the sensor glucose value is determined, atleast in part, by a filter applied to raw glucose sensor data measuredby the continuous glucose sensor.

In a third aspect, a method for calibrating glucose sensor data from acontinuous glucose sensor is provided, the method comprising:immediately calibrating a continuous glucose sensor by matching areference glucose value with a first sensor glucose value; andsubsequently calibrating the continuous glucose sensor by matching thereference glucose value with a second sensor glucose value.

In an embodiment of the third aspect, the first sensor glucose value andthe second sensor glucose value are different.

In an embodiment of the third aspect, the step of immediatelycalibrating comprises matching the reference glucose value with thefirst sensor glucose value without compensating for a time lag.

In an embodiment of the third aspect, the step of subsequentlycalibrating comprises matching the reference glucose value with thesecond sensor glucose value, whereby a time lag is compensated for.

In an embodiment of the third aspect, the step of immediatelycalibrating further comprises determining a calibration state comprisingone of an out-of-calibration state and an in-calibration state.

In an embodiment of the third aspect, the step of determining acalibration state further comprises displaying information indicative ofthe calibration state on a user interface.

In an embodiment of the third aspect, the method further comprisesdisplaying immediately calibrated sensor data after the step ofimmediately calibrating.

In an embodiment of the third aspect, the method further comprisesdisplaying subsequently calibrated sensor data after the step ofsubsequently calibrating.

In a fourth aspect, a system for calibrating an analyte sensor isprovided, the system comprising: a sensor data module configured toreceive sensor data from an analyte sensor, the sensor data comprisingat least one sensor data point; a reference input module, configured toreceive reference data from a reference analyte monitor, the referencedata comprising at least one reference analyte value; a processor moduleconfigured to match at least one sensor data point with at least onereference analyte value to form a matched data pair, wherein theprocessor module is further configured to convert sensor data into atleast one estimated analyte value utilizing the matched data pair; andan output module configured to display the estimated analyte valuewithin about 10 minutes of receiving the reference analyte value.

In a fifth aspect, a system is provided for calibrating glucose sensordata from a continuous glucose sensor, the system comprising: aprocessor module configured to obtain a reference glucose value andmatch the reference glucose value with a sensor glucose value withoutcompensating for a time lag such that a time stamp of the referenceglucose value is as close as possible to a time stamp of the sensorglucose value.

In a sixth aspect, a system is provided for calibrating glucose sensordata from a continuous glucose sensor, the system comprising: aprocessor module configured to immediately calibrate a continuousglucose sensor by matching a reference glucose value with a first sensorglucose value and subsequently calibrating the continuous glucose sensorby matching the reference glucose value with a second sensor glucosevalue.

In a seventh aspect, a system is provided for processing data from acontinuous analyte sensor, comprising: a continuous analyte sensorconfigured to continuously measure a concentration of analyte in a hostand provide continuous analyte sensor data associated therewith; and aprocessor module configured to set a mode selected from a plurality ofpredetermined modes, wherein the processor module is configured toprocess the continuous analyte sensor data based at least in part on themode.

In an embodiment of the seventh aspect, the processor module isconfigured to set the mode at least in part responsive to receipt of auser input.

In an embodiment of the seventh aspect, the system comprises one or morebuttons, and wherein the processor module is configured to receive theuser input by selection of one or more buttons.

In an embodiment of the seventh aspect, the system comprises a screen,wherein the processor module is configured to display one or more menuson the screen, and wherein the processor module is configured to receivethe user input by selection of one or more items from the one or moremenus.

In an embodiment of the seventh aspect, the system is configured tooperably connect with an external system such that data can betransmitted from the external system to the system, and wherein theprocessor module is configured to receive the user input by a datatransmission received from the external system.

In an embodiment of the seventh aspect, the operable connection is awired connection.

In an embodiment of the seventh aspect, the operable connection is awireless connection.

In an embodiment of the seventh aspect, the external system comprises aprogramming configured to schedule events on a calendar.

In an embodiment of the seventh aspect, the processor module isconfigured to automatically set the mode based at least in part on acomparison of data with one or more criteria.

In an embodiment of the seventh aspect, the system further comprises anaccelerometer, wherein the data comprises data received from theaccelerometer.

In an embodiment of the seventh aspect, the system further comprises atemperature sensor, wherein the data comprises data received from thetemperature sensor.

In an embodiment of the seventh aspect, the continuous analyte sensorcomprises a continuous glucose sensor, wherein the data comprisesglucose sensor data, and wherein the one or more criteria comprise oneor more thresholds associated with hyperglycemia and/or hypoglycemia.

In an embodiment of the seventh aspect, the processor module comprisesprogramming configured to automatically set the mode at least in partresponsive to an adaptive mode learning module, wherein the adaptivemode learning module is configured to process sensor data andtime-corresponding mode over time and subsequently modify the automaticmode-setting programming at least in part responsive thereto.

In an embodiment of the seventh aspect, the system is further configuredto provide an alarm responsive to the sensor data meeting one or morecriteria.

In an embodiment of the seventh aspect, the one or more criteria arebased at least in part on the mode.

In an embodiment of the seventh aspect, the system is configured toprovide the alarm via an audible sound, visual display, vibration,alphanumeric message, and/or wireless transmission based on the mode.

In an embodiment of the seventh aspect, the system is further configuredto determine a therapy instruction based at least in part on the mode.

In an embodiment of the seventh aspect, the system is further configuredto determine the therapy instruction based at least on the continuousanalyte sensor data.

In an embodiment of the seventh aspect, the system is operably connectedwith a medicament delivery module, and wherein the medicament deliverymodule comprises programming configured to require a validation of thetherapy instruction prior to delivery of the therapy via the medicamentdelivery module, and wherein validation requirement is dependent uponthe mode.

In an embodiment of the seventh aspect, the processor module is furtherconfigured to classify a level of noise in the continuous analyte sensordata

In an embodiment of the seventh aspect, the processor module isconfigured to set the mode responsive at least in part to the level ofnoise.

In an embodiment of the seventh aspect, the plurality of predeterminedmodes include two or more modes selected from the group consisting ofresting mode, do not disturb mode, exercise mode, illness mode,menstruation mode, mealtime mode, day mode, night mode, hypoglycemicmode, hyperglycemic mode, noise mode.

In an embodiment of the seventh aspect, the processor module furthercomprises a timer associated with one or more modes, wherein the timeris configured to set the mode for a predetermined amount of time.

In an embodiment of the seventh aspect, the timer is user settable.

In an embodiment of the seventh aspect, the processor module is furtherconfigured to set the mode at least in part responsive to a modeprofile.

In an embodiment of the seventh aspect, the mode profile is usersettable.

In an eighth aspect, a method for processing data from a continuousanalyte sensor is provided, comprising: receiving continuous analytesensor data from a continuous analyte sensor; setting a mode selectedfrom a plurality of predetermined modes; and processing the continuousanalyte sensor data based at least in part on the mode.

In an embodiment of the eighth aspect, the step of setting the modecomprises receiving a user input.

In an embodiment of the eighth aspect, the step of receiving user inputcomprises receiving a selection of one or more buttons.

In an embodiment of the eighth aspect, the step of receiving user inputcomprises receiving a selection of one or more items from the one ormore menus displayed on a screen.

In an embodiment of the eighth aspect, the step of receiving user inputcomprises receiving a data transmission from an external system.

In an embodiment of the eighth aspect, the step of receiving a datatransmission comprises receiving a wired data transmission.

In an embodiment of the eighth aspect, the step of receiving a datatransmission comprises receiving a wireless data transmission.

In an embodiment of the eighth aspect, the step of receiving a datatransmission comprises receiving a data transmission from an eventscheduling software.

In an embodiment of the eighth aspect, the step of processing comprisescomparing the continuous analyte sensor data with one or more criteria.

In an embodiment of the eighth aspect, the method further comprisesreceiving data from an accelerometer, wherein the step of setting themode is responsive at least in part to the data received from anaccelerometer.

In an embodiment of the eighth aspect, the method further comprisesreceiving data from a temperature sensor, wherein the step of settingthe mode is responsive at least in part to the received data.

In an embodiment of the eighth aspect, the step of receiving continuousanalyte sensor data comprises receiving data from a continuous glucosesensor and wherein the one or more criteria comprise one or morethresholds associated with hypoglycemia and/or hyperglycemia.

In an embodiment of the eighth aspect, the step of setting the modecomprises automatically setting the mode.

In an embodiment of the eighth aspect, the method further comprisesprocessing sensor data and time-corresponding mode settings over timeand wherein the step of automatically setting the mode is based at leastin part on one or more modes.

In an embodiment of the eighth aspect, the step of processing comprisesinitiating an alarm responsive to the sensor data meeting one or morecriteria.

In an embodiment of the eighth aspect, the one or more criteria arebased at least in part on the mode.

In an embodiment of the eighth aspect, the alarm comprises an audiblealarm, visual alarm, vibration alarm, alphanumeric message alarm, and/orwireless transmission alarm based at least in part on the mode.

In an embodiment of the eighth aspect, the step of processing comprisesdetermining a therapy instruction based at least in part on the mode.

In an embodiment of the eighth aspect, the step of determining thetherapy instruction is based at least in part on the continuous analytesensor data.

In an embodiment of the eighth aspect, the step of determining a therapyinstruction further comprises requiring a validation of the therapyinstruction prior to delivery of the therapy via the medicament deliverydevice, and wherein validation is based at least in part on the mode.

In an embodiment of the eighth aspect, the step of processing comprisesclassifying a level of noise in the continuous analyte sensor data.

In an embodiment of the eighth aspect, the step of classifying a levelof noise comprises setting the mode based at least in part on the levelof noise.

In an embodiment of the eighth aspect, the step of setting a mode from aplurality of predetermined modes comprise two or more modes selectedfrom the group consisting of resting mode, do not disturb mode, exercisemode, illness mode, menstruation mode, mealtime mode, day mode, nightmode, hypoglycemic mode, hyperglycemic mode, noise mode.

In an embodiment of the eighth aspect, the step of setting the modecomprises setting a mode for a predetermined amount of time based atleast in part upon a timer.

In an embodiment of the eighth aspect, the timer is user settable.

In an embodiment of the eighth aspect, the step of setting the modecomprises setting the mode based at least in part on a mode profile.

In an embodiment of the eighth aspect, setting the mode profile is usersettable.

In a ninth aspect, a method is provided for processing of a continuousglucose sensor signal, the method comprising: receiving sensor data froma continuous analyte sensor, including one or more sensor data points;comparing sensor data against one or more criteria for at least one ofhypoglycemia, hyperglycemia, predicted hypoglycemia, and predictedhyperglycemia; and triggering an alarm when the sensor data meets one ormore predetermined criteria.

In an embodiment of the ninth aspect, the alarm comprises first andsecond user selectable alarms.

In an embodiment of the ninth aspect, the first alarm is configured toalarm during a first time of day and wherein the second alarm isconfigured to alarm during a second time of day.

In an embodiment of the ninth aspect, the alarm is configured to turn ona light.

In an embodiment of the ninth aspect, the alarm is configured to alarm aremote device.

In an embodiment of the ninth aspect, the alarm comprises sending a textmessage to a remote device.

In a tenth aspect, a system is provided for processing continuousglucose sensor data, the system comprising: a continuous glucose sensorconfigured to generate sensor data associated with a glucoseconcentration in a host; and a computer system that compares sensor dataagainst predetermined criteria for at least one of hypoglycemia,hyperglycemia, predicted hypoglycemia and predicted hyperglycemia, andtriggers an alarm when the sensor data meets predetermined criteria.

In an embodiment of the tenth aspect, the alarm comprises first andsecond user selectable alarms.

In an embodiment of the tenth aspect, the first alarm is configured toalarm during a first time of day and wherein the second alarm isconfigured to alarm during a second time of day.

In an embodiment of the tenth aspect, the alarm is configured to turn alight on.

In an embodiment of the tenth aspect, the alarm is configured to alarm aremote device located more than about 10 feet away from the continuousglucose sensor.

In an embodiment of the tenth aspect, the alarm comprises a textmessage, and wherein the computer system is configured to send the textmessage a remote device.

In an eleventh aspect, a method for processing continuous glucose sensordata is provided, the method comprising: receiving sensor data from acontinuous glucose sensor, wherein the sensor data comprises one or moresensor data points; obtaining an estimated sensor glucose value from theone or more sensor data points; calculating at least two rate of changevalues; and filtering the at least two rate of change values to obtain afiltered rate of change value.

In an embodiment of the eleventh aspect, the at least two rate of changevalues are point-to-point rate of change values.

In an embodiment of the eleventh aspect, the method further comprisesdetermining a predicted value for a future time period based on theestimated sensor glucose value, the filtered rate of change value, and atime to the future time period.

In an embodiment of the eleventh aspect, the time to the future timeperiod is user selectable.

In an embodiment of the eleventh aspect, the method further comprisescomparing the predicted value against a threshold.

In an embodiment of the eleventh aspect, the method further comprisestriggering an alarm when the predicted value passes the threshold.

In an embodiment of the eleventh aspect, the threshold is userselectable.

In an embodiment of the eleventh aspect, the method further comprisesdetermining a predicted time to a threshold, wherein the predicted timeis based at least in part on the estimated sensor glucose value, thefiltered rate of change value, and the threshold.

In an embodiment of the eleventh aspect, the threshold is userselectable.

In an embodiment of the eleventh aspect, the method further comprisesdisplaying the predicted time to the threshold on a user interface.

In an embodiment of the eleventh aspect, the step of displaying thepredicted time to the threshold is performed only when the predictedtime is below a predetermined value

In an embodiment of the eleventh aspect, the method further comprisesdetermining an insulin therapy based at least in part on the filteredrate of change value.

In an embodiment of the eleventh aspect, the step of filtering to obtaina filtered rate of change value is performed continuously.

In an embodiment of the eleventh aspect, the step of filtering to obtaina filtered rate of change value is not performed when a level of noiseis above a threshold.

In an embodiment of the eleventh aspect, the method further comprisesdisplaying a trend arrow representative of the filtered rate of changevalues.

In a twelfth aspect, a system is provided for processing continuousglucose sensor data, the system comprising: a continuous glucose sensorconfigured to generate sensor data associated with glucose concentrationin a host; and a computer system that obtains an estimated sensorglucose value, calculates at least two rate of change values, andfilters the at least two rate of change values to obtain a filtered rateof change value.

In an embodiment of the twelfth aspect, the at least two rate of changevalues are point-to-point rate of change values.

In an embodiment of the twelfth aspect, the computer system determines apredicted value for a future time period based on the estimated sensorglucose value, the filtered rate of change value and a time to thefuture time period.

In an embodiment of the twelfth aspect, the time to the future timeperiod is user selectable.

In an embodiment of the twelfth aspect, the computer system compares thepredicted value against a threshold.

In an embodiment of the twelfth aspect, the computer system triggers analarm when the predicted value passes the threshold.

In an embodiment of the twelfth aspect, the threshold is userselectable.

In an embodiment of the twelfth aspect, the computer system determines apredicted time to a threshold, wherein the predicted time is based atleast in part on the estimated sensor glucose value, the filtered rateof change value, and a threshold

In an embodiment of the twelfth aspect, the threshold is userselectable.

In an embodiment of the twelfth aspect, the computer system isconfigured to display the predicted time to threshold on a userinterface.

In an embodiment of the twelfth aspect, the computer system isconfigured to display the predicted time to threshold only when thepredicted time is below a predetermined value.

In an embodiment of the twelfth aspect, the computer system determinesan insulin therapy based at least in part on the filtered rate of changevalue.

In an embodiment of the twelfth aspect, the computer system continuouslyfilters the at least two rate of change values to obtain a filtered rateof change value.

In an embodiment of the twelfth aspect, the computer system displays atrend arrow representative of the filtered rate of change values.

In an embodiment of the twelfth aspect, the computer system filters theat least two rate of change values to obtain a filtered rate of changevalue only when a level of noise is below a threshold.

In a thirteenth aspect, a method for determining a rate of change of acontinuous glucose sensor signal is provided, comprising: receivingsensor data from a continuous analyte sensor, the sensor data comprisingone or more sensor data points; and calculating a rate of change for awindow of sensor data, wherein the window of sensor data comprises twoor more sensor data points.

In an embodiment of the thirteenth aspect, the window of sensor datacomprises a user selectable time period.

In an embodiment of the thirteenth aspect, the window of sensor datacomprises a programmable time period.

In an embodiment of the thirteenth aspect, the window of sensor dataadaptively adjusts based at least in part on a level of noise in thesensor data.

In a fourteenth aspect, a system is provided for determining a rate ofchange of a continuous glucose sensor signal, comprising: a continuousglucose sensor configured to generate sensor data associated with aglucose concentration in a host; and a computer system configured tocalculate a rate of change for a window of sensor data, the sensor datacomprising two or more sensor data points.

In an embodiment of the fourteenth aspect, the window of sensor data isa user selectable time period.

In an embodiment of the fourteenth aspect, the window of sensor data isa programmable time period.

In an embodiment of the fourteenth aspect, the computer system isconfigured to adaptively adjust the window of sensor data based at leastin part on a level of noise in the sensor data.

In a fifteenth aspect, a method is provided for determining a rate ofchange of a continuous glucose sensor signal, comprising: receivingsensor data from a continuous analyte sensor; determining a level ofnoise in the sensor data; and calculating a rate of change for a windowof the sensor data, wherein the window of sensor data comprises two ormore sensor data points.

In an embodiment of the fifteenth aspect, the step of calculating a rateof change uses either raw sensor data or filtered sensor data, dependingat least in part upon the level of noise determined.

In an embodiment of the fifteenth aspect, the step of calculating a rateof change comprises at least two rate of change calculations, andwherein the step of calculating a rate of change further comprisesadaptively selecting a filter to apply to the at least two rate ofchange calculations based at least in part on the level of noisedetermined.

In a sixteenth aspect, a system is provided for determining a rate ofchange of a continuous glucose sensor signal, comprising: a continuousglucose sensor configured to generate sensor data associated with aglucose concentration in a host; and a computer system configured todetermine a level of noise in the sensor data and calculate a rate ofchange for a window of the sensor data, wherein the window of sensordata comprises two or more sensor data points.

In an embodiment of the sixteenth aspect, the computer system isconfigured to use either raw sensor data or filtered sensor data in therate of change calculation depending at least in part upon the level ofnoise determined.

In an embodiment of the sixteenth aspect, the rate of change calculationcomprises calculating at least two rate of change calculations, andwherein the rate of change calculation further comprises adaptivelyselecting a filter to apply to the rate of change calculation based atleast in part on the level of noise determined.

In a seventeenth aspect, a method is provided for classifying a level ofnoise in a signal obtained from a continuous glucose sensor, the methodcomprising: receiving a signal from a continuous glucose sensor; andclassifying a level of noise on the signal.

In an embodiment of the seventeenth aspect, the step of classifyingcomprises applying a low pass filter to the signal to determine a signalstrength.

In an embodiment of the seventeenth aspect, the step of classifyingcomprises defining one or more noise thresholds for classification ofthe level of noise on the signal, wherein the one or more noisethresholds are based at least in part on a percentage of the signalstrength.

In an embodiment of the seventeenth aspect, the step of classifyingcomprises applying one or more low pass filters to the noise signal toobtain one or more noise indicators and comparing the noise indicatorswith the one or more noise thresholds.

In an embodiment of the seventeenth aspect, the method further comprisesdetermining a noise signal from the sensor signal, and wherein the stepof classifying comprises applying one or more filters to the noisesignal to obtain one or more noise indicators and comparing the noiseindicators with one or more noise thresholds.

In an embodiment of the seventeenth aspect, the step of classifyingcomprises performing spectral analysis to determine at least one of asignal strength and a noise indicator.

In an embodiment of the seventeenth aspect, the step of classifying alevel of noise comprises using hysteresis.

In an embodiment of the seventeenth aspect, the method further comprisescontrolling an output based at least in part on the level of noise.

In an embodiment of the seventeenth aspect, the method further comprisescontrolling a display based at least in part on the level of noise.

In an embodiment of the seventeenth aspect, the step of controlling adisplay comprises controlling the display of raw and/or filtered databased at least in part on the level of noise.

In an embodiment of the seventeenth aspect, the step of controlling adisplay comprises displaying rate of change information based at leastin part on the level of noise.

In an embodiment of the seventeenth aspect, the method further comprisesa step of controlling at least one alarm indicative of at least one ofhypoglycemia, hyperglycemia, predicted hypoglycemia, and predictedhyperglycemia based at least in part on the level of noise.

In an embodiment of the seventeenth aspect, the method further comprisesa step of controlling medicament delivery and/or medicament therapyinstructions based at least in part on the level of noise.

In an embodiment of the seventeenth aspect, the method further comprisesa step of diagnosing a sensor condition based at least in part on thelevel of noise.

In an embodiment of the seventeenth aspect, the method further comprisesa step of suspending display of sensor data based at least in part onthe level of noise.

In an embodiment of the seventeenth aspect, the method further comprisesa step of shutting down a sensor session based at least in part on thelevel of noise.

In an embodiment of the seventeenth aspect, the method further comprisesa step of displaying the level of noise on the user interface.

In an embodiment of the seventeenth aspect, the method further comprisesa step of displaying information indicative of the level of noise on thesensor signal.

In an embodiment of the seventeenth aspect, the method further comprisesa step of displaying information indicative of an amount of time thatthe signal has been classified as having a level of noise.

In an eighteenth aspect, a system is provided for classifying a level ofnoise in a signal obtained from a continuous glucose sensor, the systemcomprising: a continuous glucose sensor that provides a signalindicative of a glucose concentration in a host; and a computer systemthat classifies a level of noise on the signal.

In an embodiment of the eighteenth aspect, the method further comprisesthe computer system filters the signal by applying a low pass filter tothe signal to determine a signal strength.

In an embodiment of the eighteenth aspect, the computer system definesone or more noise thresholds for classification of the level of noise onthe signal, and wherein the one or more noise thresholds are based atleast in part on a percentage of the signal strength.

In an embodiment of the eighteenth aspect, the computer systemclassifies the level of noise by applying one or more low pass filtersto the noise signal to obtain one or more noise indicators and comparingthe noise indicators to the one or more noise thresholds.

In an embodiment of the eighteenth aspect, the computer systemclassifies a level of noise by applying one or more filters to the noisesignal to obtain one or more noise indicators and comparing the noiseindicators with one or more noise thresholds.

In an embodiment of the eighteenth aspect, the computer systemclassifies a level of noise by performing spectral analysis to determineat least one of a signal strength and a noise indicator.

In an embodiment of the eighteenth aspect, the computer system useshysteresis to classify the level of noise.

In an embodiment of the eighteenth aspect, the computer system providesan output based at least in part on the level of noise.

In an embodiment of the eighteenth aspect, the system further comprisesa user interface configured to display the glucose concentration to thehost, wherein the computer system is configured to control the displaybased at least in part on the level of noise.

In an embodiment of the eighteenth aspect, the computer system controlsthe display of raw and/or filtered data based at least in part on thelevel of noise.

In an embodiment of the eighteenth aspect, the computer system controlsthe display of rate of change information based at least in part on thelevel of noise.

In an embodiment of the eighteenth aspect, the computer system isconfigured to control alarms indicative of at least one of hypoglycemia,hyperglycemia, predicted hypoglycemia, and predicted hyperglycemia basedat least in part on the level of noise.

In an embodiment of the eighteenth aspect, the computer system isconfigured to control medicament delivery and/or medicament therapyinstructions based at least in part on the level of noise.

In an embodiment of the eighteenth aspect, the computer system isconfigured to diagnose a sensor condition based at least in part on thelevel of noise.

In an embodiment of the eighteenth aspect, the computer system isconfigured to suspend display of a glucose concentration based at leastin part on the level of noise.

In an embodiment of the eighteenth aspect, the computer system isconfigured to shut down a sensor session based at least in part on thelevel of noise.

In an embodiment of the eighteenth aspect, the computer system isconfigured to display the level of noise on the user interface.

In an embodiment of the eighteenth aspect, the computer system isconfigured to display information indicative of the level of noise onthe sensor signal.

In an embodiment of the eighteenth aspect, the computer system isconfigured to display information indicative of an amount of time thesignal has been classified as having a level of noise.

In a nineteenth embodiment, a method is provided for calibration of acontinuous glucose sensor, the method comprising: receiving sensor datafrom a continuous analyte sensor, the sensor data comprising one or moresensor data points; receiving and processing calibration information;evaluating a predictive accuracy of calibration information; anddetermining when to request reference data based at least in part on thepredictive accuracy of calibration information.

In an embodiment of the nineteenth aspect, the step of determining whento request reference data comprises determining a time period.

In an embodiment of the nineteenth aspect, the time period is betweenabout 0 minutes and 7 days.

In an embodiment of the nineteenth aspect, the step of receiving andprocessing calibration information comprises receiving one or morematched data pairs, wherein the step of evaluating a predictive accuracycomprises evaluating a correlation of at least one matched data pairwith at least some of the calibration information.

In an embodiment of the nineteenth aspect, the step of determining isbased at least in part on the correlation of the at least one matcheddata pair and the calibration information.

In an embodiment of the nineteenth aspect, the step of receiving andprocessing calibration information comprises receiving reference datafrom a reference analyte monitor, forming at least one matched datapair, forming a calibration set including said at least one matched datapair, and forming a calibration line from said calibration set, whereinthe step of evaluating a predictive accuracy comprises evaluating acorrelation of the matched data pairs in the calibration set with thecalibration line.

In an embodiment of the nineteenth aspect, the step of determining isbased at least in part on the correlation of the matched pairs in thecalibration set and the calibration line.

In an embodiment of the nineteenth aspect, the step of receiving andprocessing calibration information comprises receiving a matched datapair and forming a calibration set including said matched data pair,wherein the step of evaluating a predictive accuracy comprisesevaluating a discordance of the matched data pair and/or the matcheddata pairs in the calibration set.

In an embodiment of the nineteenth aspect, the step of determining isbased at least in part on the discordance of the matched data pairand/or the matched data pairs in the calibration set.

In an embodiment of the nineteenth aspect, the step of receiving andprocessing calibration information comprises forming one or more matcheddata pairs by matching time corresponding sensor and reference data andforming a calibration set including one or more matched data pairswherein the step of evaluating a predictive accuracy comprisesiteratively evaluating a plurality of combinations of matched data pairsin the calibration set to obtain a plurality of calibration lines.

In an embodiment of the nineteenth aspect, the method further comprisesremoving matched data pairs from the calibration set in response to theiterative evaluation.

In an embodiment of the nineteenth aspect, the step of determining isbased at least in part on the iterative evaluation.

In an embodiment of the nineteenth aspect, the step of receiving andprocessing calibration information comprises receiving reference data,forming one or more matched data pairs, and forming a calibration setincluding one or more matched data pairs, wherein the step of evaluatinga predictive accuracy comprises evaluating an goodness of fit of thecalibration set with a calibration line drawn from the calibration set.

In an embodiment of the nineteenth aspect, the step of determining isbased at least in part on the goodness of fit.

In an embodiment of the nineteenth aspect, the step of receiving andprocessing calibration information comprises receiving reference data,and wherein the step of evaluating a predictive accuracy comprisesevaluating a leverage of the reference data based at least in part on aglucose concentration associated with the reference data.

In an embodiment of the nineteenth aspect, the step of determining isbased at least in part on the leverage of the reference data.

In an embodiment of the nineteenth aspect, the method further comprisesrequesting reference data responsive to the step of determining.

In an embodiment of the nineteenth aspect, the method further comprisesdisplaying an amount of time before reference data will be requested.

In a twentieth aspect, a system is provided for calibration of acontinuous analyte sensor, comprising: a continuous analyte sensorconfigured to continuously measure a concentration of analyte in a host;and a computer system that receives sensor data from the continuousanalyte sensor, wherein the computer system is configured to receive andprocess calibration information, and wherein the computer systemevaluates a predictive accuracy of calibration information to determinewhen to request additional reference data.

In an embodiment of the twentieth aspect, the computer system determinesa time period to request additional reference data.

In an embodiment of the twentieth aspect, the time period is betweenabout 0 minutes and 7 days.

In an embodiment of the twentieth aspect, the computer system isconfigured to receive reference data from a reference analyte monitor,wherein the computer system is configured to match reference data tosubstantially time corresponding sensor data to form at least onematched data pair, and wherein the computer system is configured toevaluate a predictive accuracy by evaluating a correlation of at leastone matched data pair with at least some of the calibration information.

In an embodiment of the twentieth aspect, the computer system determineswhen to request reference data based at least in part on the correlationof the at least one matched data pair and the calibration information.

In an embodiment of the twentieth aspect, the computer system isconfigured to receive reference data from a reference analyte monitor,match reference data to substantially time corresponding sensor data toform at least one matched data pair, form a calibration set from atleast one matched data pair, and form a calibration line from thecalibration set, wherein the computer system is configured to evaluate apredictive accuracy by evaluating a correlation of matched data pairs inthe calibration set with a calibration line based on a calibration setincluding a newly received matched data pair.

In an embodiment of the twentieth aspect, the computer system determineswhen to request reference data based at least in part on the correlationof the matched pairs in the calibration set and the calibration line.

In an embodiment of the twentieth aspect, the computer system isconfigured to receive reference data from a reference analyte monitor,match reference data to substantially time corresponding sensor data toform at least one matched data pair, and form a calibration set from atleast one matched data pair, wherein the computer system is configuredto evaluate a predictive accuracy by evaluating a discordance of amatched data pair and/or a plurality of matched data pairs in acalibration set.

In an embodiment of the twentieth aspect, the computer system determineswhen to request reference data based at least in part on the discordanceof the matched data pair and/or the matched data pairs in thecalibration set.

In an embodiment of the twentieth aspect, the computer system isconfigured to receive reference data from a reference analyte monitor,match reference data to substantially time corresponding sensor data toform at least one matched data pair, and form a calibration set from atleast one matched data pair, wherein the computer system iterativelyevaluates a plurality of combinations of matched data pairs in thecalibration set to obtain a plurality of calibration lines.

In an embodiment of the twentieth aspect, the computer system isconfigured to remove matched data pairs from the calibration set inresponse to the iterative evaluation.

In an embodiment of the twentieth aspect, the computer system determineswhen to request reference data based at least in part on the iterativeevaluation.

In an embodiment of the twentieth aspect, the computer system isconfigured to receive reference data from a reference analyte monitor,match reference data to substantially time corresponding sensor data toform at least one matched data pair, and form a calibration set from atleast one matched data pair, wherein the computer system is configuredto evaluate a predictive accuracy by evaluating a goodness of fit of thecalibration set with a calibration line drawn from the calibration set.

In an embodiment of the twentieth aspect, the computer system determineswhen to request reference data based at least in part on the goodness offit.

In an embodiment of the twentieth aspect, the computer system isconfigured to receive reference data from a reference analyte monitor,and wherein the computer system is configured to evaluate a predictiveaccuracy by evaluating a leverage of the reference data based at leastin part on a glucose concentration associated with reference data.

In an embodiment of the twentieth aspect, the computer system determineswhen to request reference data based at least in part on leverage of thereference data.

In an embodiment of the twentieth aspect, the computer system isconfigured to request reference data at a time determined by thepredictive evaluation.

In an embodiment of the twentieth aspect, the computer system isconfigured to display an amount of time before reference data will berequested.

In a twenty-first aspect, a method is provided for calibration of acontinuous glucose sensor, the method comprising: receiving a signalfrom a continuous glucose sensor, evaluating a sensor performance duringa sensor session; and determining when to request reference dataresponsive to the sensor performance determined.

In an embodiment of the twenty-first aspect, the step of evaluating asensor performance comprises determining an amount of drift on thesensor signal over a time period, and wherein the step of determiningwhen to request reference data comprises requesting reference data whenthe amount of drift is greater than a threshold.

In an embodiment of the twenty-first aspect, the step of determining anamount of drift comprises monitoring a change in signal strength.

In an embodiment of the twenty-first aspect, the step of determining anamount of drift comprises analyzing a fluctuation in a second workingelectrode of a dual electrode system.

In an embodiment of the twenty-first aspect, the step of monitoring achange in signal strength comprises applying a low pass filter.

In an embodiment of the twenty-first aspect, the step of determining anamount of drift comprises monitoring a change in calibrationinformation.

In an embodiment of the twenty-first aspect, the method furthercomprises controlling an output in response to the sensor performance.

In a twenty-second aspect, a method is provided for calibration of acontinuous glucose sensor, the method comprising: receiving a signalfrom a continuous glucose sensor; determining a predictive accuracy ofsensor calibration; and controlling an output based at least in part onthe predictive accuracy determined.

In an embodiment of the twenty-second aspect, the step of controlling anoutput comprises controlling a display of data based at least in part onthe level of noise.

In an embodiment of the twenty-second aspect, the step of controlling anoutput comprises controlling alarms indicative of at least one ofhypoglycemia, hyperglycemia, predicted hypoglycemia, and predictedhyperglycemia based at least in part on the predictive accuracy.

In an embodiment of the twenty-second aspect, the method furthercomprises controlling insulin delivery and/or insulin therapyinstructions based at least in part on the predictive accuracy.

In an embodiment of the twenty-second aspect, the method furthercomprises diagnosing a sensor condition based at least in part on thepredictive accuracy.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is an exploded perspective view of a glucose sensor in oneembodiment.

FIG. 1B is side view of a distal portion of a transcutaneously insertedsensor in one embodiment.

FIG. 1C is a cross-sectional schematic view of a sensing region of adual-electrode continuous analyte sensor in one embodiment wherein anactive enzyme of an enzyme domain is positioned over the first workingelectrode but not over the second working electrode.

FIG. 2 is a block diagram that illustrates sensor electronics in oneembodiment.

FIGS. 3A to 3D are schematic views of a receiver in first, second,third, and fourth embodiments, respectively.

FIG. 4A is a block diagram of receiver electronics in one embodiment.

FIG. 4B is an illustration of the receiver in one embodiment showing ananalyte trend graph, including measured analyte values, estimatedanalyte values, and a clinical risk zone.

FIG. 4C is an illustration of the receiver in another embodiment showinga representation of analyte concentration and directional trend using agradient bar.

FIG. 4D is an illustration of the receiver in yet another embodiment,including a screen that shows a numerical representation of the mostrecent measured analyte value.

FIG. 5 is a block diagram of an integrated system of the preferredembodiments, including a continuous glucose sensor, a receiver forprocessing and displaying sensor data, a medicament delivery device, andan optional single point glucose-monitoring device.

FIG. 6A is a flow chart that illustrates the process of calibrating thesensor data in one embodiment.

FIG. 6B is a graph that illustrates a linear regression used tocalibrate the sensor data in one embodiment.

FIG. 6C is a flow chart that illustrates the process of immediatecalibration of a continuous analyte sensor in one embodiment.

FIG. 7 is a flow chart that illustrates the process of smart orintelligent calibration of a continuous analyte sensor in oneembodiment.

FIG. 8A is a graph illustrating the components of a signal measured by atranscutaneous glucose sensor (after sensor break-in was complete),implanted in a non-diabetic, human volunteer host.

FIG. 8B is a graph that shows a raw data stream obtained from a glucosesensor over a 4 hour time span in one example.

FIG. 8C is a graph that shows a raw data stream obtained from a glucosesensor over a 36 hour time span in another example.

FIG. 9 is a flow chart that illustrates the process of detecting andreplacing transient non-glucose related signal artifacts in a datastream in one embodiment.

FIG. 10 is a graph that illustrates a method of classifying noise in adata stream from a glucose sensor in one embodiment.

FIG. 11 is a flow chart that illustrates a method of detecting andprocessing signal artifacts in the data stream from a glucose sensor inone embodiment.

FIG. 12 is a graph that illustrates a raw data stream from a glucosesensor for approximately 24 hours with a filtered version of the samedata stream superimposed on the same graph.

FIG. 13 is a flow chart that illustrates a method of calculating a rateof change of sensor data from a glucose sensor in one embodiment.

FIG. 14 is a flow chart that illustrates a method of predictinghypoglycemic and/or hyperglycemic episodes based on continuous glucosesensor data in one embodiment.

FIG. 15 is a flow chart that illustrates a method of setting a mode andfurther processing data based upon a mode setting in one embodiment.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The following description and examples illustrate some exemplaryembodiments of the disclosed invention in detail. Those of skill in theart will recognize that there are numerous variations and modificationsof this invention that are encompassed by its scope. Accordingly, thedescription of a certain exemplary embodiment should not be deemed tolimit the scope of the present invention.

Definitions

In order to facilitate an understanding of the preferred embodiments, anumber of terms are defined below.

The term “analyte” as used herein is a broad term and is to be given itsordinary and customary meaning to a person of ordinary skill in the art(and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to a substance or chemicalconstituent in a biological fluid (for example, blood, interstitialfluid, cerebral spinal fluid, lymph fluid or urine) that can beanalyzed. Analytes can include naturally occurring substances,artificial substances, metabolites, and/or reaction products. In someembodiments, the analyte for measurement by the sensor heads, devices,and methods is analyte. However, other analytes are contemplated aswell, including but not limited to acarboxyprothrombin; acylcarnitine;adenine phosphoribosyl transferase; adenosine deaminase; albumin;alpha-fetoprotein; amino acid profiles (arginine (Krebs cycle),histidine/urocanic acid, homocysteine, phenylalanine/tyrosine,tryptophan); andrenostenedione; antipyrine; arabinitol enantiomers;arginase; benzoylecgonine (cocaine); biotinidase; biopterin; c-reactiveprotein; carnitine; carnosinase; CD4; ceruloplasmin; chenodeoxycholicacid; chloroquine; cholesterol; cholinesterase; conjugated 1-βhydroxy-cholic acid; cortisol; creatine kinase; creatine kinase MMisoenzyme; cyclosporin A; d-penicillamine; de-ethylchloroquine;dehydroepiandrosterone sulfate; DNA (acetylator polymorphism, alcoholdehydrogenase, alpha 1-antitrypsin, cystic fibrosis, Duchenne/Beckermuscular dystrophy, analyte-6-phosphate dehydrogenase, hemoglobin A,hemoglobin S, hemoglobin C, hemoglobin D, hemoglobin E, hemoglobin F,D-Punjab, beta-thalassemia, hepatitis B virus, HCMV, HIV-1, HTLV-1,Leber hereditary optic neuropathy, MCAD, RNA, PKU, Plasmodium vivax,sexual differentiation, 21-deoxycortisol); desbutylhalofantrine;dihydropteridine reductase; diptheria/tetanus antitoxin; erythrocytearginase; erythrocyte protoporphyrin; esterase D; fattyacids/acylglycines; free β-human chorionic gonadotropin; freeerythrocyte porphyrin; free thyroxine (FT4); free tri-iodothyronine(FT3); fumarylacetoacetase; galactose/gal-1-phosphate;galactose-1-phosphate uridyltransferase; gentamicin; analyte-6-phosphatedehydrogenase; glutathione; glutathione perioxidase; glycocholic acid;glycosylated hemoglobin; halofantrine; hemoglobin variants;hexosaminidase A; human erythrocyte carbonic anhydrase I;17-alpha-hydroxyprogesterone; hypoxanthine phosphoribosyl transferase;immunoreactive trypsin; lactate; lead; lipoproteins ((a), B/A-1, β);lysozyme; mefloquine; netilmicin; phenobarbitone; phenytoin;phytanic/pristanic acid; progesterone; prolactin; prolidase; purinenucleoside phosphorylase; quinine; reverse tri-iodothyronine (rT3);selenium; serum pancreatic lipase; sissomicin; somatomedin C; specificantibodies (adenovirus, anti-nuclear antibody, anti-zeta antibody,arbovirus, Aujeszky's disease virus, dengue virus, Dracunculusmedinensis, Echinococcus granulosus, Entamoeba histolytica, enterovirus,Giardia duodenalisa, Helicobacter pylori, hepatitis B virus, herpesvirus, HIV-1, IgE (atopic disease), influenza virus, Leishmaniadonovani, leptospira, measles/mumps/rubella, Mycobacterium leprae,Mycoplasma pneumoniae, Myoglobin, Onchocerca volvulus, parainfluenzavirus, Plasmodium falciparum, poliovirus, Pseudomonas aeruginosa,respiratory syncytial virus, rickettsia (scrub typhus), Schistosomamansoni, Toxoplasma gondii, Trepenoma pallidium, Trypanosomacruzi/rangeli, vesicular stomatis virus, Wuchereria bancrofti, yellowfever virus); specific antigens (hepatitis B virus, HIV-1);succinylacetone; sulfadoxine; theophylline; thyrotropin (TSH); thyroxine(T4); thyroxine-binding globulin; trace elements; transferrin;UDP-galactose-4-epimerase; urea; uroporphyrinogen I synthase; vitamin A;white blood cells; and zinc protoporphyrin. Salts, sugar, protein, fat,vitamins, and hormones naturally occurring in blood or interstitialfluids can also constitute analytes in certain embodiments. The analytecan be naturally present in the biological fluid, for example, ametabolic product, a hormone, an antigen, an antibody, and the like.Alternatively, the analyte can be introduced into the body, for example,a contrast agent for imaging, a radioisotope, a chemical agent, afluorocarbon-based synthetic blood, or a drug or pharmaceuticalcomposition, including but not limited to insulin; ethanol; cannabis(marijuana, tetrahydrocannabinol, hashish); inhalants (nitrous oxide,amyl nitrite, butyl nitrite, chlorohydrocarbons, hydrocarbons); cocaine(crack cocaine); stimulants (amphetamines, methamphetamines, Ritalin,Cylert, Preludin, Didrex, PreState, Voranil, Sandrex, Plegine);depressants (barbituates, methaqualone, tranquilizers such as Valium,Librium, Miltown, Serax, Equanil, Tranxene); hallucinogens(phencyclidine, lysergic acid, mescaline, peyote, psilocybin); narcotics(heroin, codeine, morphine, opium, meperidine, Percocet, Percodan,Tussionex, Fentanyl, Darvon, Talwin, Lomotil); designer drugs (analogsof fentanyl, meperidine, amphetamines, methamphetamines, andphencyclidine, for example, Ecstasy); anabolic steroids; and nicotine.The metabolic products of drugs and pharmaceutical compositions are alsocontemplated analytes. Analytes such as neurochemicals and otherchemicals generated within the body can also be analyzed, such as, forexample, ascorbic acid, uric acid, dopamine, noradrenaline,3-methoxytyramine (3MT), 3,4-Dihydroxyphenylacetic acid (DOPAC),Homovanillic acid (HVA), 5-Hydroxytryptamine (5HT), and5-Hydroxyindoleacetic acid (FHIAA).

The term “ROM” as used herein is a broad term and is to be given itsordinary and customary meaning to a person of ordinary skill in the art(and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to read-only memory, which is atype of data storage device manufactured with fixed contents. ROM isbroad enough to include EEPROM, for example, which is electricallyerasable programmable read-only memory (ROM).

The term “RAM” as used herein is a broad term and is to be given itsordinary and customary meaning to a person of ordinary skill in the art(and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to a data storage device for whichthe order of access to different locations does not affect the speed ofaccess. RAM is broad enough to include RAM, for example, which is staticrandom access memory that retains data bits in its memory as long aspower is being supplied.

The term “A/D Converter” as used herein is a broad term and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to hardware and/orsoftware that converts analog electrical signals into correspondingdigital signals.

The terms “processor module,” “microprocessor” and “processor” as usedherein are broad terms and are to be given their ordinary and customarymeaning to a person of ordinary skill in the art (and are not to belimited to a special or customized meaning), and furthermore referwithout limitation to a computer system, state machine, and the likethat performs arithmetic and logic operations using logic circuitry thatresponds to and processes the basic instructions that drive a computer.

The term “RF transceiver” as used herein is a broad term and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to a radio frequencytransmitter and/or receiver for transmitting and/or receiving signals.

The term “jitter” as used herein is a broad term and is to be given itsordinary and customary meaning to a person of ordinary skill in the art(and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to noise above and below the meancaused by ubiquitous noise caused by a circuit and/or environmentaleffects; jitter can be seen in amplitude, phase timing, or the width ofthe signal pulse.

The terms “raw data stream” and “data stream” as used herein are broadterms and are to be given their ordinary and customary meaning to aperson of ordinary skill in the art (and are not to be limited to aspecial or customized meaning), and furthermore refer without limitationto an analog or digital signal directly related to the measured glucosefrom the glucose sensor. In one example, the raw data stream is digitaldata in “counts” converted by an A/D converter from an analog signal(e.g., voltage or amps) and includes one or more data pointsrepresentative of a glucose concentration. The terms broadly encompass aplurality of time spaced data points from a substantially continuousglucose sensor, which comprises individual measurements taken at timeintervals ranging from fractions of a second up to, e.g., 1, 2, or 5minutes or longer. In another example, the raw data stream includes anintegrated digital value, wherein the data includes one or more datapoints representative of the glucose sensor signal averaged over a timeperiod.

The term “calibration” as used herein is a broad term and is to be givenits ordinary and customary meaning to a person of ordinary skill in theart (and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to the process of determining therelationship between the sensor data and the corresponding referencedata, which can be used to convert sensor data into meaningful valuessubstantially equivalent to the reference data. In some embodiments,namely, in continuous analyte sensors, calibration can be updated orrecalibrated over time as changes in the relationship between the sensordata and reference data occur, for example, due to changes insensitivity, baseline, transport, metabolism, and the like.

The terms “calibrated data” and “calibrated data stream” as used hereinare broad terms and are to be given their ordinary and customary meaningto a person of ordinary skill in the art (and are not to be limited to aspecial or customized meaning), and furthermore refer without limitationto data that has been transformed from its raw state to another stateusing a function, for example a conversion function, to provide ameaningful value to a user.

The terms “smoothed data” and “filtered data” as used herein are broadterms and are to be given their ordinary and customary meaning to aperson of ordinary skill in the art (and are not to be limited to aspecial or customized meaning), and furthermore refer without limitationto data that has been modified to make it smoother and more continuousand/or to remove or diminish outlying points, for example, by performinga moving average of the raw data stream. Examples of data filtersinclude FIR (finite impulse response), IIR (infinite impulse response),moving average filters, and the like.

The terms “smoothing” and “filtering” as used herein are broad terms andare to be given their ordinary and customary meaning to a person ofordinary skill in the art (and are not to be limited to a special orcustomized meaning), and furthermore refer without limitation to amathematical computation that attenuates components of a signal that areundesired, such as reducing noise errors in a signal. In someembodiments, smoothing refers to modification of a set of data to makeit smoother and more continuous or to remove or diminish outlyingpoints, for example, by performing a moving average of the raw datastream.

The term “noise signal” as used herein is a broad term and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to a signalassociated with noise on the data stream (e.g., non-analyte relatedsignal). The noise signal can be determined by filtering and/oraveraging, for example. In some embodiments, the noise signal is asignal residual, delta residual (difference of residual), absolute deltaresidual, and/or the like, which are described in more detail elsewhereherein.

The term “algorithm” as used herein is a broad term and is to be givenits ordinary and customary meaning to a person of ordinary skill in theart (and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to a computational process (forexample, programs) involved in transforming information from one stateto another, for example, by using computer processing.

The term “matched data pairs” as used herein is a broad term and is tobe given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to reference data(for example, one or more reference analyte data points) matched withsubstantially time corresponding sensor data (for example, one or moresensor data points).

The term “counts” as used herein is a broad term and is to be given itsordinary and customary meaning to a person of ordinary skill in the art(and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to a unit of measurement of adigital signal. In one example, a raw data stream measured in counts isdirectly related to a voltage (e.g., converted by an A/D converter),which is directly related to current from the working electrode. Inanother example, counter electrode voltage measured in counts isdirectly related to a voltage.

The term “sensor” as used herein is a broad term and is to be given itsordinary and customary meaning to a person of ordinary skill in the art(and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to the component or region of adevice by which an analyte can be quantified and/or the device itself.

The terms “glucose sensor” and “member for determining the amount ofglucose in a biological sample” as used herein are broad terms and areto be given their ordinary and customary meaning to a person of ordinaryskill in the art (and are not to be limited to a special or customizedmeaning), and furthermore refer without limitation to any mechanism(e.g., enzymatic or non-enzymatic) by which glucose can be quantified.For example, some embodiments utilize a membrane that contains glucoseoxidase that catalyzes the conversion of oxygen and glucose to hydrogenperoxide and gluconate, as illustrated by the following chemicalreaction:Glucose+O₂→Gluconate+H₂O₂

Because for each glucose molecule metabolized, there is a proportionalchange in the co-reactant O₂ and the product H₂O₂, one can use anelectrode to monitor the current change in either the co-reactant or theproduct to determine glucose concentration.

The terms “operably connected” and “operably linked” as used herein arebroad terms and are to be given their ordinary and customary meaning toa person of ordinary skill in the art (and are not to be limited to aspecial or customized meaning), and furthermore refer without limitationto one or more components being linked to another component(s) in amanner that allows transmission of signals between the components. Forexample, one or more electrodes can be used to detect the amount ofglucose in a sample and convert that information into a signal, e.g., anelectrical or electromagnetic signal; the signal can then be transmittedto an electronic circuit. In this case, the electrode is “operablylinked” to the electronic circuitry. These terms are broad enough toinclude wireless connectivity.

The term “electronic circuitry” as used herein is a broad term and is tobe given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to the components ofa device configured to process biological information obtained from ahost. In the case of a glucose-measuring device, the biologicalinformation is obtained by a sensor regarding a particular glucose in abiological fluid, thereby providing data regarding the amount of thatglucose in the fluid. U.S. Pat. Nos. 4,757,022, 5,497,772 and 4,787,398,which are hereby incorporated by reference, describe suitable electroniccircuits that can be utilized with devices including the biointerfacemembrane of a preferred embodiment.

The term “substantially” as used herein is a broad term and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to being largely butnot necessarily wholly that which is specified.

The term “host” as used herein is a broad term and is to be given itsordinary and customary meaning to a person of ordinary skill in the art(and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to mammals, particularly humans.

The term “continuous analyte (or glucose) sensor” as used herein is abroad term and is to be given its ordinary and customary meaning to aperson of ordinary skill in the art (and is not to be limited to aspecial or customized meaning), and furthermore refers withoutlimitation to a device that continuously or continually measures aconcentration of an analyte, for example, at time intervals ranging fromfractions of a second up to, for example, 1, 2, or 5 minutes, or longer.In one exemplary embodiment, the continuous analyte sensor is a glucosesensor such as described in U.S. Pat. No. 6,001,067, which isincorporated herein by reference in its entirety.

The term “continuous analyte (or glucose) sensing” as used herein is abroad term and is to be given its ordinary and customary meaning to aperson of ordinary skill in the art (and is not to be limited to aspecial or customized meaning), and furthermore refers withoutlimitation to the period in which monitoring of an analyte iscontinuously or continually performed, for example, at time intervalsranging from fractions of a second up to, for example, 1, 2, or 5minutes, or longer.

The terms “reference analyte monitor,” “reference analyte meter,” and“reference analyte sensor” as used herein are broad terms and are to begiven their ordinary and customary meaning to a person of ordinary skillin the art (and are not to be limited to a special or customizedmeaning), and furthermore refer without limitation to a device thatmeasures a concentration of an analyte and can be used as a referencefor the continuous analyte sensor, for example a self-monitoring bloodglucose meter (SMBG) can be used as a reference for a continuous glucosesensor for comparison, calibration, and the like.

The term “Clarke Error Grid”, as used herein, is a broad term and is tobe given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and refers without limitation to an error grid analysis, whichevaluates the clinical significance of the difference between areference glucose value and a sensor generated glucose value, takinginto account 1) the value of the reference glucose measurement, 2) thevalue of the sensor glucose measurement, 3) the relative differencebetween the two values, and 4) the clinical significance of thisdifference. See Clarke et al., “Evaluating Clinical Accuracy of Systemsfor Self-Monitoring of Blood Glucose”, Diabetes Care, Volume 10, Number5, September-October 1987, which is incorporated by reference herein inits entirety.

The term “Consensus Error Grid”, as used herein, is a broad term and isto be given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and refers without limitation to an error grid analysis thatassigns a specific level of clinical risk to any possible error betweentwo time corresponding glucose measurements. The Consensus Error Grid isdivided into zones signifying the degree of risk posed by the deviation.See Parkes et al., “A New Consensus Error Grid to Evaluate the ClinicalSignificance of Inaccuracies in the Measurement of Blood Glucose”,Diabetes Care, Volume 23, Number 8, August 2000, which is incorporatedby reference herein in its entirety.

The term “clinical acceptability”, as used herein, is a broad term andis to be given its ordinary and customary meaning to a person ofordinary skill in the art (and is not to be limited to a special orcustomized meaning), and refers without limitation to determination ofthe risk of inaccuracies to a patient. Clinical acceptability considersa deviation between time corresponding glucose measurements (e.g., datafrom a glucose sensor and data from a reference glucose monitor) and therisk (e.g., to the decision making of a diabetic patient) associatedwith that deviation based on the glucose value indicated by the sensorand/or reference data. One example of clinical acceptability may be 85%of a given set of measured analyte values within the “A” and “B” regionof a standard Clarke Error Grid when the sensor measurements arecompared to a standard reference measurement.

The term “R-value,” as used herein is a broad term and is to be givenits ordinary and customary meaning to a person of ordinary skill in theart (and is not to be limited to a special or customized meaning), andrefers without limitation to one conventional way of summarizing thecorrelation of data; that is, a statement of what residuals (e.g., rootmean square deviations) are to be expected if the data are fitted to astraight line by the a regression.

The terms “data association” and “data association function,” as usedherein, are broad terms and are to be given their ordinary and customarymeaning to a person of ordinary skill in the art (and are not to belimited to a special or customized meaning), and refer withoutlimitation to a statistical analysis of data and particularly itscorrelation to, or deviation from, from a particular curve. A dataassociation function is used to show data association. For example, datathat form as calibration set as described herein may be analyzedmathematically to determine its correlation to, or deviation from, acurve (e.g., line or set of lines) that defines the conversion function;this correlation or deviation is the data association. A dataassociation function is used to determine data association. Examples ofdata association functions include, but are not limited to, linearregression, non-linear mapping/regression, rank (e.g., non-parametric)correlation, least mean square fit, mean absolute deviation (MAD), meanabsolute relative difference. In one such example, the correlationcoefficient of linear regression is indicative of the amount of dataassociation of the calibration set that forms the conversion function,and thus the quality of the calibration.

The term “quality of calibration” as used herein, is a broad term and isto be given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and refers without limitation to the statistical associationof matched data pairs in the calibration set used to create theconversion function. For example, an R-value may be calculated for acalibration set to determine its statistical data association, whereinan R-value greater than 0.79 determines a statistically acceptablecalibration quality, while an R-value less than 0.79 determinesstatistically unacceptable calibration quality.

The terms “congruence” and “correlation” as used herein, are broad termsand are to be given their ordinary and customary meaning to a person ofordinary skill in the art (and are not to be limited to a special orcustomized meaning), and refer without limitation to the quality orstate of agreeing, coinciding, or being concordant. In one example,correlation may be determined using a data association function.

The term “discordance” as used herein, is a broad term and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and refers without limitation to the disassociation ofcomparative data. In one example, discordance may be determined using adata association function.

The phrase “goodness of fit” as used herein, is a broad term and is tobe given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and refers without limitation to a degree to which a modelfits the observed data. For example, in a regression analysis, thegoodness-of-fit can be quantified in terms of R-squared, R-value and/orerror distribution.

The term “sensor session” as used herein, is a broad term and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and refers without limitation to a period of time a sensor isin use, such as but not limited to a period of time starting at the timethe sensor is implanted (e.g., by the host) to removal of the sensor(e.g., removal of the sensor from the host's body and/or removal of thetransmitter from the sensor housing).

The terms “sensor head” and “sensing region” as used herein are broadterms and are to be given their ordinary and customary meaning to aperson of ordinary skill in the art (and are not to be limited to aspecial or customized meaning), and furthermore refer without limitationto the region of a monitoring device responsible for the detection of aparticular analyte. The sensing region generally comprises anon-conductive body, a working electrode (anode), a reference electrode(optional), and/or a counter electrode (cathode) passing through andsecured within the body forming electrochemically reactive surfaces onthe body and an electronic connective means at another location on thebody, and a multi-domain membrane affixed to the body and covering theelectrochemically reactive surface.

The term “physiologically feasible” as used herein is a broad term andis to be given its ordinary and customary meaning to a person ofordinary skill in the art (and is not to be limited to a special orcustomized meaning), and furthermore refers without limitation to thephysiological parameters obtained from continuous studies of glucosedata in humans and/or animals. For example, a maximal sustained rate ofchange of glucose in humans of about 4 to 5 mg/dL/min and a maximumacceleration of the rate of change of about 0.1 to 0.2 mg/dL/min/min aredeemed physiologically feasible limits. Values outside of these limitswould be considered non-physiological and likely a result of signalerror, for example. As another example, the rate of change of glucose islowest at the maxima and minima of the daily glucose range, which arethe areas of greatest risk in patient treatment, thus a physiologicallyfeasible rate of change can be set at the maxima and minima based oncontinuous studies of glucose data. As a further example, it has beenobserved that the best solution for the shape of the curve at any pointalong glucose signal data stream over a certain time period (e.g., about20 to 30 minutes) is a straight line, which can be used to setphysiological limits.

The term “system noise” as used herein is a broad term and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to unwantedelectronic or diffusion-related noise which can include Gaussian,motion-related, flicker, kinetic, or other white noise, for example.

The terms “noise,” “noise event(s),” “noise episode(s),” “signalartifact(s),” “signal artifact event(s),” and “signal artifactepisode(s)” as used herein are broad terms and are to be given theirordinary and customary meaning to a person of ordinary skill in the art(and are not to be limited to a special or customized meaning), andfurthermore refer without limitation to signal noise that is caused bysubstantially non-glucose related, such as interfering species, macro-or micro-motion, ischemia, pH changes, temperature changes, pressure,stress, or even unknown sources of mechanical, electrical and/orbiochemical noise for example. In some embodiments, signal artifacts aretransient and characterized by a higher amplitude than system noise, anddescribed as “transient non-glucose related signal artifact(s) that havea higher amplitude than system noise.” In some embodiments, noise iscaused by rate-limiting (or rate-increasing) phenomena. In somecircumstances, the source of the noise is unknown.

The terms “constant noise” and “constant background” as used herein arebroad terms, and are to be given their ordinary and customary meaning toa person of ordinary skill in the art (and are not to be limited to aspecial or customized meaning), and refer without limitation to thecomponent of the noise signal that remains relatively constant overtime. In some embodiments, constant noise may be referred to as“background” or “baseline.” For example, certain electroactive compoundsfound in the human body are relatively constant factors (e.g., baselineof the host's physiology). In some circumstances, constant backgroundnoise can slowly drift over time (e.g., increase or decrease), howeverthis drift need not adversely affect the accuracy of a sensor, forexample, because a sensor can be calibrated and re-calibrated and/or thedrift measured and compensated for.

The terms “non-constant noise,” “non-constant background,” “noiseevent(s),” “noise episode(s),” “signal artifact(s),” “signal artifactevent(s),” and “signal artifact episode(s)” as used herein are broadterms, and are to be given their ordinary and customary meaning to aperson of ordinary skill in the art (and are not to be limited to aspecial or customized meaning), and refer without limitation to acomponent of the background signal (e.g., non-analyte related signal)that is relatively non-constant, for example, transient and/orintermittent. For example, certain electroactive compounds, arerelatively non-constant due to the host's ingestion, metabolism, woundhealing, and other mechanical, chemical and/or biochemical factors),which create intermittent (e.g., non-constant) “noise” on the sensorsignal that can be difficult to “calibrate out” using a standardcalibration equations (e.g., because the background of the signal doesnot remain constant).

The term “linear regression” as used herein is a broad term and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to finding a line inwhich a set of data has a minimal measurement from that line. Byproductsof this algorithm include a slope, a y-intercept, and an R-Squared valuethat determine how well the measurement data fits the line.

The term “non-linear regression” as used herein is a broad term and isto be given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to fitting a set ofdata to describe the relationship between a response variable and one ormore explanatory variables in a non-linear fashion.

The term “mean” as used herein is a broad term and is to be given itsordinary and customary meaning to a person of ordinary skill in the art(and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to the sum of the observationsdivided by the number of observations.

The term “trimmed mean” as used herein is a broad term and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to a mean takenafter extreme values in the tails of a variable (e.g., highs and lows)are eliminated or reduced (e.g., “trimmed”). The trimmed meancompensates for sensitivities to extreme values by dropping a certainpercentage of values on the tails. For example, the 50% trimmed mean isthe mean of the values between the upper and lower quartiles. The 90%trimmed mean is the mean of the values after truncating the lowest andhighest 5% of the values. In one example, two highest and two lowestmeasurements are removed from a data set and then the remainingmeasurements are averaged.

The term “non-recursive filter” as used herein is a broad term and is tobe given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to an equation thatuses moving averages as inputs and outputs.

The terms “recursive filter” and “auto-regressive algorithm” as usedherein are broad terms and are to be given their ordinary and customarymeaning to a person of ordinary skill in the art (and are not to belimited to a special or customized meaning), and furthermore referwithout limitation to an equation in which includes previous averagesare part of the next filtered output. More particularly, the generationof a series of observations whereby the value of each observation ispartly dependent on the values of those that have immediately precededit. One example is a regression structure in which lagged responsevalues assume the role of the independent variables.

The term “signal estimation algorithm factors” as used herein is a broadterm and is to be given its ordinary and customary meaning to a personof ordinary skill in the art (and is not to be limited to a special orcustomized meaning), and furthermore refers without limitation to one ormore algorithms that use historical and/or present signal data streamvalues to estimate unknown signal data stream values. For example,signal estimation algorithm factors can include one or more algorithms,such as linear or non-linear regression. As another example, signalestimation algorithm factors can include one or more sets ofcoefficients that can be applied to one algorithm.

The terms “physiological parameters” and “physiological boundaries” asused herein are broad terms and are to be given their ordinary andcustomary meaning to a person of ordinary skill in the art (and are notto be limited to a special or customized meaning), and furthermore referwithout limitation to the parameters obtained from continuous studies ofphysiological data in humans and/or animals. For example, a maximalsustained rate of change of glucose in humans of about 4 to 5 mg/dL/minand a maximum acceleration of the rate of change of about 0.1 to 0.2mg/dL/min² are deemed physiologically feasible limits; values outside ofthese limits would be considered non-physiological. As another example,the rate of change of glucose is lowest at the maxima and minima of thedaily glucose range, which are the areas of greatest risk in patienttreatment, thus a physiologically feasible rate of change can be set atthe maxima and minima based on continuous studies of glucose data. As afurther example, it has been observed that the best solution for theshape of the curve at any point along glucose signal data stream over acertain time period (for example, about 20 to 30 minutes) is a straightline, which can be used to set physiological limits. These terms arebroad enough to include physiological parameters for any analyte.

The term “measured analyte values” as used herein is a broad term and isto be given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to an analyte valueor set of analyte values for a time period for which analyte data hasbeen measured by an analyte sensor. The term is broad enough to includedata from the analyte sensor before or after data processing in thesensor and/or receiver (for example, data smoothing, calibration, andthe like).

The term “estimated analyte values” as used herein is a broad term andis to be given its ordinary and customary meaning to a person ofordinary skill in the art (and is not to be limited to a special orcustomized meaning), and furthermore refers without limitation to ananalyte value or set of analyte values, which have been algorithmicallyextrapolated from measured analyte values. In some embodiments,estimated analyte values are estimated for a time period during which nodata exists. However, estimated analyte values can also be estimatedduring a time period for which measured data exists, but is to bereplaced by algorithmically extrapolated (e.g. processed or filtered)data due to noise or a time lag in the measured data, for example.

The term “calibration information” as used herein is a broad term and isto be given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to any informationuseful in calibration of a sensor. Calibration information includesreference data received from a reference analyte monitor, including oneor more reference data points, one or more matched data pairs formed bymatching reference data (e.g., one or more reference glucose datapoints) with substantially time corresponding sensor data (e.g., one ormore continuous sensor data points), a calibration set formed from a setof one or more matched data pairs, and/or a calibration line drawn fromthe calibration set, for example.

The term “mode” as used herein is a broad term and is to be given itsordinary and customary meaning to a person of ordinary skill in the art(and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to an automatic and/or userconfigurable setting within a system associated with an activity, event,physiological condition, sensor condition, and/or preference of a user.

The term “mode profile” as used herein is a broad term and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to a programmable,predetermined, and/or user selectable sequence of modes based on time.In one embodiment, the mode profile enables an automated setting ofmodes based upon a mode profile, which can be associated with, forexample, a schedule of events or blocks of events corresponding tovarious times throughout their day.

As employed herein, the following abbreviations apply: Eq and Eqs(equivalents); mEq (milliequivalents); M (molar); mM (millimolar) μM(micromolar); N (Normal); mol (moles); mmol (millimoles); μmol(micromoles); nmol (nanomoles); g (grams); mg (milligrams); ∞g(micrograms); Kg (kilograms); L (liters); mL (milliliters); dL(deciliters); μL (microliters); cm (centimeters); mm (millimeters); μm(micrometers); nm (nanometers); h and hr (hours); min. (minutes); s andsec. (seconds); ° C. (degrees Centigrade).

Overview

The preferred embodiments relate to the use of an analyte sensor thatmeasures a concentration of glucose or a substance indicative of theconcentration or presence of the analyte. In some embodiments, theanalyte sensor is a continuous device, for example a subcutaneous,transdermal, or intravascular device. In some embodiments, the devicecan analyze a plurality of intermittent blood samples. The analytesensor can use any method of glucose-measurement, including enzymatic,chemical, physical, electrochemical, spectrophotometric, polarimetric,calorimetric, iontophoretic, radiometric and the like.

The analyte sensor can use any known method, including invasive,minimally invasive, and non-invasive sensing techniques, to provide adata stream indicative of the concentration of the analyte in a host.The data stream is typically a raw data signal that is used to provide auseful value of the analyte to a user, such as a patient or doctor, whomay be using the sensor.

Sensor

Although much of the description and examples are drawn to a glucosesensor, the systems and methods of the preferred embodiments can beapplied to any measurable analyte. In some preferred embodiments, theanalyte sensor is a glucose sensor capable of measuring theconcentration of glucose in a host. One exemplary embodiment isdescribed below, which utilizes an implantable glucose sensor. However,it should be understood that the devices and methods described hereincan be applied to any device capable of detecting a concentration ofanalyte and providing an output signal that represents the concentrationof the analyte.

In one preferred embodiment, the analyte sensor is an implantableglucose sensor, such as described with reference to U.S. Pat. No.6,001,067 and U.S. Patent Publication No. US-2005-0027463-A1. In anotherpreferred embodiment, the analyte sensor is a transcutaneous glucosesensor, such as described with reference to U.S. Patent Publication No.US-2006-0020187-A1. In yet another preferred embodiment, the analytesensor is a dual electrode analyte sensor, such as described withreference to U.S. patent application Ser. No. 12/055,149. In still otherembodiments, the sensor is configured to be implanted in a host vesselor extracorporeally, such as is described in U.S. Patent Publication No.US-2007-0027385-A1, co-pending U.S. patent application Ser. No.11/543,396 filed Oct. 4, 2006, co-pending U.S. patent application Ser.No. 11/691,426 filed on Mar. 26, 2007, and co-pending U.S. patentapplication Ser. No. 11/675,063 filed on Feb. 14, 2007. In onealternative embodiment, the continuous glucose sensor comprises atranscutaneous sensor such as described in U.S. Pat. No. 6,565,509 toSay et al., for example. In another alternative embodiment, thecontinuous glucose sensor comprises a subcutaneous sensor such asdescribed with reference to U.S. Pat. No. 6,579,690 to Bonnecaze et al.or U.S. Pat. No. 6,484,046 to Say et al., for example. In anotheralternative embodiment, the continuous glucose sensor comprises arefillable subcutaneous sensor such as described with reference to U.S.Pat. No. 6,512,939 to Colvin et al., for example. In another alternativeembodiment, the continuous glucose sensor comprises an intravascularsensor such as described with reference to U.S. Pat. No. 6,477,395 toSchulman et al., for example. In another alternative embodiment, thecontinuous glucose sensor comprises an intravascular sensor such asdescribed with reference to U.S. Pat. No. 6,424,847 to Mastrototaro etal.

FIG. 1A is an exploded perspective view of one exemplary embodimentcomprising an implantable glucose sensor 100A that utilizes amperometricelectrochemical sensor technology to measure glucose concentration. Inthis exemplary embodiment, a body 110 and head 112 house the electrodes114 and sensor electronics, which are described in more detail belowwith reference to FIG. 2. Three electrodes 114 are operably connected tothe sensor electronics (FIG. 2) and are covered by a sensing membrane116 and a biointerface membrane 118, which are attached by a clip 119.

In one embodiment, the three electrodes 114, which protrude through thehead 112, include a platinum working electrode, a platinum counterelectrode, and a silver/silver chloride reference electrode. The topends of the electrodes are in contact with an electrolyte phase (notshown), which is a free-flowing fluid phase disposed between the sensingmembrane 116 and the electrodes 114. The sensing membrane 116 includesan enzyme, e.g., glucose oxidase, which covers the electrolyte phase.The biointerface membrane 118 covers the sensing membrane 116 andserves, at least in part, to protect the sensor 100A from externalforces that can result in environmental stress cracking of the sensingmembrane 116.

In the illustrated embodiment, the counter electrode is provided tobalance the current generated by the species being measured at theworking electrode. In the case of a glucose oxidase based glucosesensor, the species being measured at the working electrode is H₂O₂.Glucose oxidase catalyzes the conversion of oxygen and glucose tohydrogen peroxide and gluconate according to the following reaction:Glucose+O₂→Gluconate+H₂O₂

The change in H₂O₂ can be monitored to determine glucose concentrationbecause for each glucose molecule metabolized, there is a proportionalchange in the product H₂O₂. Oxidation of H₂O₂ by the working electrodeis balanced by reduction of ambient oxygen, enzyme generated H₂O₂, orother reducible species at the counter electrode. The H₂O₂ produced fromthe glucose oxidase reaction further reacts at the surface of workingelectrode and produces two protons (2H⁺), two electrons (2e⁻), and oneoxygen molecule (O₂).

FIG. 1B is side view of a distal portion 120 of a transcutaneously- orintravascularly-inserted sensor 100B in one embodiment, showing workingand reference electrodes. In preferred embodiments, the sensor 100B isformed from a working electrode 122 and a reference electrode 124helically wound around the working electrode 122. An insulator 126 isdisposed between the working and reference electrodes to providenecessary electrical insulation there between. Certain portions of theelectrodes are exposed to enable electrochemical reaction thereon, forexample, a window 128 can be formed in the insulator to expose a portionof the working electrode 122 for electrochemical reaction.

In preferred embodiments, each electrode is formed from a fine wire witha diameter of from about 0.001 or less to about 0.010 inches or more,for example, and is formed from, e.g. a plated insulator, a plated wire,or bulk electrically conductive material. Although the illustratedelectrode configuration and associated text describe one preferredmethod of forming a sensor, a variety of known sensor configurations canbe employed with the analyte sensor system of the preferred embodiments,such as are described in U.S. Pat. No. 6,695,860 to Ward et al., U.S.Pat. No. 6,565,509 to Say et al., U.S. Pat. No. 6,248,067 to Causey III,et al., and U.S. Pat. No. 6,514,718 to Heller et al.

In preferred embodiments, the working electrode comprises a wire formedfrom a conductive material, such as platinum, platinum-iridium,palladium, graphite, gold, carbon, conductive polymer, alloys, and thelike. Although the electrodes can by formed by a variety ofmanufacturing techniques (bulk metal processing, deposition of metalonto a substrate, and the like), it can be advantageous to form theelectrodes from plated wire (e.g. platinum on steel wire) or bulk metal(e.g. platinum wire). It is believed that electrodes formed from bulkmetal wire provide superior performance (e.g. in contrast to depositedelectrodes), including increased stability of assay, simplifiedmanufacturability, resistance to contamination (e.g. which can beintroduced in deposition processes), and improved surface reaction (e.g.due to purity of material) without peeling or delamination.

The working electrode 122 is configured to measure the concentration ofan analyte. In an enzymatic electrochemical sensor for detectingglucose, for example, the working electrode measures the hydrogenperoxide produced by an enzyme catalyzed reaction of the analyte beingdetected and creates a measurable electronic current. For example, inthe detection of glucose wherein glucose oxidase produces hydrogenperoxide as a byproduct, hydrogen peroxide reacts with the surface ofthe working electrode producing two protons (2H⁺), two electrons (2e⁻)and one molecule of oxygen (O₂), which produces the electronic currentbeing detected.

In preferred embodiments, the working electrode 122 is covered with aninsulating material 126, for example, a non-conductive polymer.Dip-coating, spray-coating, vapor-deposition, or other coating ordeposition techniques can be used to deposit the insulating material onthe working electrode. In one embodiment, the insulating materialcomprises parylene, which can be an advantageous polymer coating for itsstrength, lubricity, and electrical insulation properties. Generally,parylene is produced by vapor deposition and polymerization ofpara-xylylene (or its substituted derivatives). However, any suitableinsulating material can be used, for example, fluorinated polymers,polyethyleneterephthalate, polyurethane, polyimide, other nonconductingpolymers, and the like. Glass or ceramic materials can also be employed.Other materials suitable for use include surface energy modified coatingsystems such as are marketed under the trade names AMC18, AMC148,AMC141, and AMC321 by Advanced Materials Components Express ofBellafonte, Pa. In some alternative embodiments, however, the workingelectrode may not require a coating of insulator.

The reference electrode 124, which can function as a reference electrodealone, or as a dual reference and counter electrode, is formed fromsilver, silver/silver chloride, and the like. Preferably, the referenceelectrode 124 is juxtapositioned and/or twisted with or around a wire122 that forms the working electrode 128; however other configurationsare also possible. In the illustrated embodiments, the referenceelectrode 124 is helically wound around the wire 122. The assembly ofwires is then optionally coated or adhered together with an insulatingmaterial, similar to that described above, so as to provide aninsulating attachment.

In embodiments wherein an outer insulator is disposed, a portion of thecoated assembly structure can be stripped or otherwise removed, forexample, by hand, excimer lasing, chemical etching, laser ablation,grit-blasting (e.g. with sodium bicarbonate or other suitable grit), andthe like, to expose the electroactive surfaces. Alternatively, a portionof the electrode can be masked prior to depositing the insulator inorder to maintain an exposed electroactive surface area.

In the embodiment illustrated in FIG. 1B, a radial window is formedthrough the insulating material 126 to expose a circumferentialelectroactive surface of the working electrode 128. Additionally,sections 129 of electroactive surface of the reference electrode areexposed. For example, the 129 sections of electroactive surface can bemasked during deposition of an outer insulating layer or etched afterdeposition of an outer insulating layer.

In some alternative embodiments, additional electrodes can be includedwithin the assembly, for example, a three-electrode system (working,reference, and counter electrodes) and/or an additional workingelectrode (e.g. an electrode which can be used to generate oxygen, whichis configured as a baseline subtracting electrode, or which isconfigured for measuring additional analytes) as described in moredetail elsewhere herein. U.S. Patent Publication No. US-2005-0161346-A1and U.S. Patent Publication No. US-2005-0143635-A1 describe some systemsand methods for implementing and using additional working, counter,and/or reference electrodes.

Preferably, a membrane system is deposited over the electroactivesurfaces of the sensor 100B and includes a plurality of domains orlayers. The membrane system may be deposited on the exposedelectroactive surfaces using known thin film techniques (for example,spraying, electro-depositing, dipping, and the like). In one exemplaryembodiment, each domain is deposited by dipping the sensor into asolution and drawing out the sensor at a speed that provides theappropriate domain thickness. In general, the membrane system may bedisposed over (e.g., deposited on) the electroactive surfaces usingmethods appreciated by one skilled in the art.

In some embodiments, the sensing membranes and/or membrane systemsinclude a plurality of domains or layers, for example, an interferencedomain, an enzyme domain, and a resistance domain, and may includeadditional domains, such as an electrode domain, a cell impermeabledomain (also referred to as a bioprotective layer), and/or an oxygendomain, as described in more detail in co-pending U.S. patentapplication Ser. No. 11/750,907 filed on May 18, 2007 and entitled“ANALYTE SENSORS HAVING A SIGNAL-TO-NOISE RATIO SUBSTANTIALLY UNAFFECTEDBY NON-CONSTANT NOISE,” which is incorporated herein by reference in itsentirety. However, it is understood that a sensing membrane modified forother sensors, for example, by including fewer or additional domains iswithin the scope of some embodiments. In some embodiments, one or moredomains of the sensing membranes are formed from materials such assilicone, polytetrafluoroethylene, polyethylene-co-tetrafluoroethylene,polyolefin, polyester, polycarbonate, biostable polytetrafluoroethylene,homopolymers, copolymers, terpolymers of polyurethanes, polypropylene(PP), polyvinylchloride (PVC), polyvinylidene fluoride (PVDF),polybutylene terephthalate (PBT), polymethylmethacrylate (PMMA),polyether ether ketone (PEEK), polyurethanes, cellulosic polymers,poly(ethylene oxide), poly(propylene oxide) and copolymers and blendsthereof, polysulfones and block copolymers thereof including, forexample, di-block, tri-block, alternating, random and graft copolymers.U.S. Patent Publication No. US-2005-024579912-A1, which is incorporatedherein by reference in its entirety, describes biointerface and sensingmembrane configurations and materials that may be applied to someembodiments.

In one exemplary embodiment, the sensor is an enzyme-basedelectrochemical sensor, wherein the glucose-measuring working electrodemeasures the hydrogen peroxide produced by the enzyme catalyzed reactionof glucose being detected and creates a measurable electronic current(for example, detection of glucose utilizing glucose oxidase producesH₂O₂ peroxide as a byproduct, H₂O₂ reacts with the surface of theworking electrode producing two protons (2H⁺), two electrons (2e⁻) andone molecule of oxygen (O₂) which produces the electronic current beingdetected), such as described in more detail above and as is appreciatedby one skilled in the art. Typically, the working and referenceelectrodes operatively connect with sensor electronics, such asdescribed in more detail elsewhere herein. Additional aspects of theabove-described transcutaneously inserted sensor can be found inco-pending U.S. Patent Publication No. US-2006-0020187-A1.

FIG. 1C is a cross-sectional schematic view of a sensing region of adual-electrode analyte sensor in one embodiment wherein an active enzymeof an enzyme domain is positioned over the first working electrode butnot over the second working electrode, such as described with referenceto U.S. patent application Ser. No. 12/055,149, which is incorporatedherein by reference in its entirety. In general, electrochemical analytesensors provide at least one working electrode and at least onereference electrode, which are configured to generate a signalassociated with a concentration of the analyte in the host, such asdescribed herein, and as appreciated by one skilled in the art. Theoutput signal is typically a raw data stream that is used to provide auseful value of the measured analyte concentration in a host to thepatient or doctor, for example. However, the analyte sensors of thepreferred embodiments may further measure at least one additionalsignal. For example, in some embodiments, the additional signal isassociated with the baseline and/or sensitivity of the analyte sensor,thereby enabling monitoring of baseline and/or sensitivity changes thatmay occur in a continuous analyte sensor over time.

In preferred embodiments, the analyte sensor comprises a first workingelectrode E1 and a second working electrode E2, in addition to areference electrode, which is referred to as a dual-electrode systemherein. The first and second working electrodes may be in any usefulconformation, as described in US Patent Publications Nos.US-2007-0027385-A1, US-2007-0213611-A1, US-2007-0027284-A1,US-2007-0032717-A1, US-2007-0093704, and U.S. patent application Ser.No. 11/865,572 filed on Oct. 1, 2007 and entitled “DUAL-ELECTRODE SYSTEMFOR A CONTINUOUS ANALYTE SENSOR,” each of which is incorporated hereinby reference in its entirety. In some preferred embodiments, the firstand second working electrodes are twisted and/or bundled. For example,two wire working electrodes can be twisted together, such as in a helixconformation. The reference electrode can then be wrapped around thetwisted pair of working electrodes. In some preferred embodiments, thefirst and second working electrodes include a coaxial configuration. Avariety of dual-electrode system configurations are described withreference to FIGS. 7A1 through 11 of the references incorporated above,for example. In some embodiments, the sensor is configured as a dualelectrode sensor, such as described in US Patent Publication Nos.US-2005-0143635-A1; US-2007-0027385-A1; and US-2007-0213611-A1, andco-pending U.S. patent application Ser. No. 11/865,572, each of which isincorporated herein by reference in its entirety. However, adual-electrode system can be provided in any planar or non-planarconfiguration, such as can be appreciated by one skilled in the art, andcan be found in U.S. Pat. No. 6,175,752 to Say et al.; U.S. Pat. No.6,579,690 to Bonnecaze et al.; U.S. Pat. No. 6,484,046 to Say et al.;U.S. Pat. No. 6,512,939 to Colvin et al.; U.S. Pat. No. 6,477,395 toSchulman et al.; U.S. Pat. No. 6,424,847 to Mastrototaro et al; U.S.Pat. No. 6,212,416 to Ward et al.; U.S. Pat. No. 6,119,028 to Schulmanet al.; U.S. Pat. No. 6,400,974 to Lesho; U.S. Pat. No. 6,595,919 toBerner et al.; U.S. Pat. No. 6,141,573 to Kurnik et al.; U.S. Pat. No.6,122,536 to Sun et al.; European Patent Application EP 1153571 toVarall et al.; U.S. Pat. No. 6,512,939 to Colvin et al.; U.S. Pat. No.5,605,152 to Slate et al.; U.S. Pat. No. 4,431,004 to Bessman et al.;U.S. Pat. No. 4,703,756 to Gough et al.; U.S. Pat. No. 6,514,718 toHeller et al.; U.S. Pat. No. 5,985,129 to Gough et al.; WO PatentApplication Publication No. 04/021877 to Caduff; U.S. Pat. No. 5,494,562to Maley et al.; U.S. Pat. No. 6,120,676 to Heller et al.; and U.S. Pat.No. 6,542,765 to Guy et al., each of which are incorporated in theirentirety herein by reference in their entirety. In general, it isunderstood that the disclosed embodiments are applicable to a variety ofcontinuous analyte measuring device configurations

The dual-electrode sensor system includes a first working electrode E1and the second working electrode E2, both of which are disposed beneatha sensor membrane M02. The first working electrode E1 is disposedbeneath an active enzymatic portion M04 of the sensor membrane M02,which includes an enzyme configured to detect the analyte or ananalyte-related compound. Accordingly, the first working electrode E1 isconfigured to generate a first signal composed of both signal related tothe analyte and signal related to non-analyte electroactive compounds(e.g., physiological baseline, interferents, and non-constant noise)that have an oxidation/reduction potential that overlaps with theoxidation/reduction potential of the analyte. This oxidation/reductionpotential may be referred to as a “first oxidation/reduction potential”herein. The second working electrode E2 is disposed beneath aninactive-enzymatic or non-enzymatic portion M06 of the sensor membraneM02. The non-enzymatic portion M06 of the membrane includes either aninactivated form of the enzyme contained in the enzymatic portion M04 ofthe membrane or no enzyme. In some embodiments, the non-enzymaticportion M06 can include a non-specific protein, such as BSA, ovalbumin,milk protein, certain polypeptides, and the like. The non-enzymaticportion M06 generates a second signal associated with noise of theanalyte sensor. The noise of the sensor comprises signal contributiondue to non-analyte electroactive species (e.g., interferents) that havean oxidation/reduction potential that substantially overlaps the firstoxidation/reduction potential (e.g., that overlap with theoxidation/reduction potential of the analyte). In some embodiments of adual-electrode analyte sensor configured for fluid communication with ahost's circulatory system, the non-analyte related electroactive speciescomprises at least one species selected from the group consisting ofinterfering species, non-reaction-related hydrogen peroxide, and otherelectroactive species.

In one exemplary embodiment, the dual-electrode analyte sensor is aglucose sensor having a first working electrode E1 configured togenerate a first signal associated with both glucose and non-glucoserelated electroactive compounds that have a first oxidation/reductionpotential. Non-glucose related electroactive compounds can be anycompound, in the sensor's local environment that has anoxidation/reduction potential substantially overlapping with theoxidation/reduction potential of H₂O₂, for example. While not wishing tobe bound by theory, it is believed that the glucose-measuring electrodecan measure both the signal directly related to the reaction of glucosewith GOx (produces H₂O₂ that is oxidized at the working electrode) andsignals from unknown compounds that are in the tissue or bloodsurrounding the sensor. These unknown compounds can be constant ornon-constant (e.g., intermittent or transient) in concentration and/oreffect. In some circumstances, it is believed that some of these unknowncompounds are related to the host's disease state. For example, it isknown that tissue/blood chemistry changes dramatically during/after aheart attack (e.g., pH changes, changes in the concentration of variousblood components/protein, and the like). Additionally, a variety ofmedicaments or infusion fluid components (e.g., acetaminophen, ascorbicacid, dopamine, ibuprofen, salicylic acid, tolbutamide, tetracycline,creatinine, uric acid, ephedrine, L-dopa, methyl dopa and tolazamide)that may be given to the host may have oxidation/reduction potentialsthat overlap with that of H₂O₂.

In this exemplary embodiment, the dual-electrode analyte sensor includesa second working electrode E2 that is configured to generate a secondsignal associated with the non-glucose related electroactive compoundsthat have the same oxidation/reduction potential as the above-describedfirst working electrode (e.g., para supra). In some embodiments, thenon-glucose related electroactive species includes at least one ofinterfering species, non-reaction-related H₂O₂, and other electroactivespecies. For example, interfering species includes any compound that isnot directly related to the electrochemical signal generated by theglucose-GOx reaction, such as but not limited to electroactive speciesin the local environment produces by other bodily processes (e.g.,cellular metabolism, a disease process, and the like). Otherelectroactive species includes any compound that has anoxidation/reduction potential similar to or overlapping that of H₂O₂.

The non-analyte (e.g., non-glucose) signal produced by compounds otherthan the analyte (e.g., glucose) obscures the signal related to theanalyte, contributes to sensor inaccuracy, and is considered backgroundnoise. Background noise includes both constant and non-constantcomponents and must be removed to accurately calculate the analyteconcentration. While not wishing to be bound by theory, it is believedthat preferred dual electrode sensors are designed such that the firstand second electrodes are influenced by substantially the sameexternal/environmental factors, which enables substantially equivalentmeasurement of both the constant and non-constant species/noise. Thisadvantageously allows the substantial elimination of noise on the sensorsignal (using electronics described elsewhere herein) to substantiallyreduce or eliminate signal effects due to noise, including non-constantnoise (e.g., unpredictable biological, biochemical species, medicaments,pH fluctuations, O₂ fluctuations, or the like) known to effect theaccuracy of conventional continuous sensor signals. Preferably, thesensor includes electronics operably connected to the first and secondworking electrodes. The electronics are configured to provide the firstand second signals that are used to generate glucose concentration datasubstantially without signal contribution due to non-glucose-relatednoise. Preferably, the electronics include at least a potentiostat thatprovides a bias to the electrodes. In some embodiments, sensorelectronics are configured to measure the current (or voltage) toprovide the first and second signals. The first and second signals areused to determine the glucose concentration substantially without signalcontribution due to non-glucose-related noise such as by but not limitedto subtraction of the second signal from the first signal or alternativedata analysis techniques. In some embodiments, the sensor electronicsinclude a transmitter that transmits the first and second signals to areceiver, where additional data analysis and/or calibration of glucoseconcentration can be processed. U.S. Patent Publication No.US-2005-0027463-A1, US-2005-0203360-A1 and U.S. Patent Publication No.US-2006-0036142-A1 describes systems and methods for processing sensoranalyte data and is incorporated herein by reference in their entirety.

In preferred embodiments, the dual-electrode sensor includes electronics(e.g., a processor module, processing memory) that are operablyconnected to the first and second working electrodes and are configuredto provide the first and second signals to generate analyteconcentration data substantially without signal contribution due tonon-analyte-related noise. For example, the sensor electronics processand/or analyze the signals from the first and second working electrodesand calculate the portion of the first electrode signal that is due toanalyte concentration only. The portion of the first electrode signalthat is not due to the analyte concentration can be considered to bebackground, such as but not limited to noise. Accordingly, in oneembodiment of a dual-electrode sensor system configured for fluidcommunication with a host's circulatory system (e.g., via a vascularaccess device) the system comprising electronics operably connected tothe first and second working electrodes; the electronics are configuredto process the first and second signals to generate analyteconcentration data substantially without signal contribution due tonoise.

In various embodiments, the electrodes can be stacked or grouped similarto that of a leaf spring configuration, wherein layers of electrode andinsulator (or individual insulated electrodes) are stacked in offsetlayers. The offset layers can be held together with bindings ofnon-conductive material, foil, or wire. As is appreciated by one skilledin the art, the strength, flexibility, and/or other material property ofthe leaf spring-configured or stacked sensor can be either modified(e.g., increased or decreased), by varying the amount of offset, theamount of binding, thickness of the layers, and/or materials selectedand their thicknesses, for example.

In preferred embodiments, the analyte sensor substantially continuouslymeasures the host's analyte concentration. In some embodiments, forexample, the sensor can measure the analyte concentration every fractionof a second, about every fraction of a minute or every minute. In otherexemplary embodiments, the sensor measures the analyte concentrationabout every 2, 3, 4, 5, 6, 7, 8, 9, or 10 minutes. In still otherembodiments, the sensor measures the analyte concentration everyfraction of an hour, such as but not limited to every 15, 30 or 45minutes. Yet in other embodiments, the sensor measures the analyteconcentration about every hour or longer. In some exemplary embodiments,the sensor measures the analyte concentration intermittently orperiodically. In one preferred embodiment, the analyte sensor is aglucose sensor and measures the host's glucose concentration about every4-6 minutes. In a further embodiment, the sensor measures the host'sglucose concentration every 5 minutes.

In some embodiments (e.g., sensors such as illustrated in FIGS. 1A, 1B,and 1C), a potentiostat is employed to monitor the electrochemicalreaction at the electrochemical cell. The potentiostat applies aconstant potential to the working and reference electrodes to determinea current value. The current that is produced at the working electrode(and flows through the circuitry to the counter electrode) isproportional to the amount of H₂O₂ that diffuses to the workingelectrode. Accordingly, a raw signal can be produced that isrepresentative of the concentration of glucose in the user's body, andtherefore can be utilized to estimate a meaningful glucose value, suchas described herein.

One problem with raw data stream output of some enzymatic glucosesensors such as described above is caused by transient non-glucosereaction rate-limiting phenomena. For example, if oxygen is deficient,relative to the amount of glucose, then the enzymatic reaction will belimited by oxygen rather than glucose. Consequently, the output signalwill be indicative of the oxygen concentration rather than the glucoseconcentration, producing erroneous signals. Other non-glucose reactionrate-limiting phenomena could include interfering species, temperatureand/or pH changes, or even unknown sources of mechanical, electricaland/or biochemical noise, for example.

FIG. 2 is a block diagram that illustrates one possible configuration ofthe sensor electronics 200 in one embodiment. In this embodiment, apotentiostat 210 is shown, which is operatively connected to anelectrode system (FIG. 1A or 1B) and provides a voltage to theelectrodes, which biases the sensor to enable measurement of a currentvalue indicative of the analyte concentration in the host (also referredto as the analog portion). In some embodiments, the potentiostatincludes a resistor (not shown) that translates the current intovoltage. In some alternative embodiments, a current to frequencyconverter is provided that is configured to continuously integrate themeasured current, for example, using a charge counting device. In theillustrated embodiment, an A/D converter 212 digitizes the analog signalinto “counts” for processing. Accordingly, the resulting raw data streamin counts is directly related to the current measured by thepotentiostat 210.

A processor module 214 is the central control unit that controls theprocessing of the sensor electronics. In some embodiments, the processormodule includes a microprocessor, however a computer system other than amicroprocessor can be used to process data as described herein, forexample an ASIC can be used for some or all of the sensor's centralprocessing. The processor typically provides semi-permanent storage ofdata, for example, storing data such as sensor identifier (ID) andprogramming to process data streams (for example, programming for datasmoothing and/or replacement of signal artifacts such as is described inmore detail elsewhere herein). The processor additionally can be usedfor the system's cache memory, for example for temporarily storingrecent sensor data. In some embodiments, the processor module comprisesmemory storage components such as ROM, RAM, dynamic-RAM, static-RAM,non-static RAM, EEPROM, rewritable ROMs, flash memory, and the like. Inone exemplary embodiment, ROM 216 provides semi-permanent storage ofdata, for example, storing data such as sensor identifier (ID) andprogramming to process data streams (e.g., programming for signalartifacts detection and/or replacement such as described elsewhereherein). In one exemplary embodiment, RAM 218 can be used for thesystem's cache memory, for example for temporarily storing recent sensordata.

In some embodiments, the processor module comprises a digital filter,for example, an IIR or FIR filter, configured to smooth the raw datastream from the A/D converter. Generally, digital filters are programmedto filter data sampled at a predetermined time interval (also referredto as a sample rate). In some embodiments, wherein the potentiostat isconfigured to measure the analyte at discrete time intervals, these timeintervals determine the sample rate of the digital filter. In somealternative embodiments, wherein the potentiostat is configured tocontinuously measure the analyte, for example, using acurrent-to-frequency converter, the processor module can be programmedto request a digital value from the A/D converter at a predeterminedtime interval, also referred to as the acquisition time. In thesealternative embodiments, the values obtained by the processor areadvantageously averaged over the acquisition time due the continuity ofthe current measurement. Accordingly, the acquisition time determinesthe sample rate of the digital filter. In preferred embodiments, theprocessor module is configured with a programmable acquisition time,namely, the predetermined time interval for requesting the digital valuefrom the A/D converter is programmable by a user within the digitalcircuitry of the processor module. An acquisition of time from about 2seconds to about 512 seconds is preferred; however any acquisition timecan be programmed into the processor module. A programmable acquisitiontime is advantageous in optimizing noise filtration, time lag, andprocessing/battery power.

In some embodiments, the processor module is configured to build thedata packet for transmission to an outside source, for example, an RFtransmission to a receiver as described in more detail below. Generally,the data packet comprises a plurality of bits that can include asensor/transmitter ID code, raw data, filtered data, and/or errordetection or correction. The processor module can be configured totransmit any combination of raw and/or filtered data.

A battery 220 is operatively connected to the processor 214 and providesthe necessary power for the sensor (e.g., 100A or 100B). In oneembodiment, the battery is a Lithium Manganese Dioxide battery, howeverany appropriately sized and powered battery can be used (e.g., AAA,Nickel-cadmium, Zinc-carbon, Alkaline, Lithium, Nickel-metal hydride,Lithium-ion, Zinc-air, Zinc-mercury oxide, Silver-zinc, orhermetically-sealed). In some embodiments the battery is rechargeable.In some embodiments, a plurality of batteries can be used to power thesystem. In yet other embodiments, the receiver can be transcutaneouslypowered via an inductive coupling, for example. A Quartz Crystal 222 isoperatively connected to the processor 214 and maintains system time forthe computer system as a whole.

An optional RF module (e.g., an RF Transceiver) 224 is operablyconnected to the processor 214 and transmits the sensor data from thesensor (e.g., 100A or 100B) to a receiver (see FIGS. 3 and 4). Althoughan RF transceiver is shown here, some other embodiments can include awired rather than wireless connection to the receiver. A second quartzcrystal 226 provides the system time for synchronizing the datatransmissions from the RF transceiver. It is noted that the transceiver224 can be substituted with a transmitter in other embodiments. In somealternative embodiments, however, other mechanisms, such as optical,infrared radiation (IR), ultrasonic, and the like, can be used totransmit and/or receive data.

In some embodiments, a Signal Artifacts Detector 228 is provided thatincludes one or more of the following: an oxygen detector 228 a, a pHdetector 228 b, a temperature detector 228 c, and a pressure/stressdetector 228 d, which is described in more detail with reference tosignal artifacts detection. It is noted that in some embodiments thesignal artifacts detector 228 is a separate entity (e.g., temperaturedetector) operatively connected to the processor, while in otherembodiments, the signal artifacts detector is a part of the processorand utilizes readings from the electrodes, for example, to detectischemia and other signal artifacts. Although the above description isfocused on an embodiment of the Signal Artifacts Detector within thesensor, some embodiments provide for systems and methods for detectingsignal artifacts in the sensor and/or receiver electronics (e.g.,processor module) as described in more detail elsewhere herein.

Receiver

FIGS. 3A to 3D are schematic views of a receiver 300 includingrepresentations of estimated glucose values on its user interface infirst, second, third, and fourth embodiments, respectively. The receiver300 comprises systems to receive, process, and display sensor data fromthe glucose sensor (e.g., 100A or 100B), such as described herein.Particularly, the receiver 300 can be a pager-sized device, for example,and comprise a user interface that has a plurality of buttons 302 and aliquid crystal display (LCD) screen 304, and which can optionallyinclude a backlight. In some embodiments, the user interface can alsoinclude a keyboard, a speaker, and a vibrator, as described below withreference to FIG. 4A.

FIG. 3A illustrates a first embodiment wherein the receiver 300 shows anumeric representation of the estimated glucose value on its userinterface, which is described in more detail elsewhere herein.

FIG. 3B illustrates a second embodiment wherein the receiver 300 showsan estimated glucose value and approximately one hour of historicaltrend data on its user interface, which is described in more detailelsewhere herein.

FIG. 3C illustrates a third embodiment wherein the receiver 300 shows anestimated glucose value and approximately three hours of historicaltrend data on its user interface, which is described in more detailelsewhere herein.

FIG. 3D illustrates a fourth embodiment wherein the receiver 300 showsan estimated glucose value and approximately nine hours of historicaltrend data on its user interface, which is described in more detailelsewhere herein.

In some embodiments, a user can toggle through some or all of thescreens shown in FIGS. 3A to 3D using a toggle button on the receiver.In some embodiments, the user will be able to interactively select thetype of output displayed on their user interface. In other embodiments,the sensor output can have alternative configurations.

FIG. 4A is a block diagram that illustrates one possible configurationof the receiver's electronics. It is noted that the receiver 300 cancomprise a configuration such as described with reference to FIGS. 3A to3D, above. Alternatively, the receiver 300 can comprise otherconfigurations, including a phone, insulin pump, desktop computer,laptop computer, a personal digital assistant (PDA), a server (local orremote to the receiver), and the like. In some embodiments, the receiver300 can be adapted to connect (via wired or wireless connection) to aphone, insulin pump, desktop computer, laptop computer, PDA, server(local or remote to the receiver), and the like, in order to downloaddata from the receiver 300. In some alternative embodiments, thereceiver 300 and/or receiver electronics can be housed within ordirectly connected to the sensor (e.g., 100A or 100B) in a manner thatallows sensor and receiver electronics to work directly together and/orshare data processing resources. Accordingly, the receiver's electronicscan be generally referred to as a “computer system.”

A quartz crystal 402 is operatively connected to an optional RFtransceiver 404 that together function to receive and synchronize datastreams (e.g., raw data streams transmitted from the RF transceiver).Once received, whether via wired or wireless transmission, a processor406 processes the signals, such as described below.

The processor 406, also referred to as the processor module, is thecentral control unit that performs the processing, such as storing data,analyzing data streams, calibrating analyte sensor data, detectingsignal artifacts, classifying a level of noise, calculating a rate ofchange, predicting analyte values, setting of modes, estimating analytevalues, comparing estimated analyte values with time correspondingmeasured analyte values, analyzing a variation of estimated analytevalues, downloading data, and controlling the user interface byproviding analyte values, prompts, messages, warnings, alarms, and thelike. The processor includes hardware and software that performs theprocessing described herein, for example flash memory provides permanentor semi-permanent storage of data, storing data such as sensor ID,receiver ID, and programming to process data streams (for example,programming for performing estimation and other algorithms describedelsewhere herein) and random access memory (RAM) stores the system'scache memory and is helpful in data processing.

In one exemplary embodiment, the processor is a microprocessor thatprovides the processing, such as calibration algorithms stored within aROM 408. The ROM 408 is operatively connected to the processor 406 andprovides semi-permanent storage of data, storing data such as receiverID and programming to process data streams (e.g., programming forperforming calibration and other algorithms described elsewhere herein).In this exemplary embodiment, an RAM 410 is used for the system's cachememory and is helpful in data processing. The term “processor module”can include some portions or all of ROM 408 and RAM 410 in addition tothe processor 406.

A battery 412 is operatively connected to the processor 406 and providespower for the receiver. In one embodiment, the battery is a standard AAAalkaline battery, however any appropriately sized and powered batterycan be used. In some embodiments, a plurality of batteries can be usedto power the system. A quartz crystal 414 is operatively connected tothe processor 406 and maintains system time for the computer system as awhole.

A user interface 416 comprises a keyboard 416 a, speaker 416 b, vibrator416 c, backlight 416 d, liquid crystal display (LCD 416 e), and one ormore buttons 416 f. The components that comprise the user interface 416provide controls to interact with the user. The keyboard 416 a canallow, for example, input of user information about himself/herself,such as mealtime, exercise, insulin administration, and referenceglucose values. The speaker 416 b can provide, for example, audiblesignals or alerts for conditions such as present and/or predicted hyper-and hypoglycemic conditions. The vibrator 416 c can provide, forexample, tactile signals or alerts for reasons such as described withreference to the speaker, above. The backlight 416 d can be provided,for example, to aid the user in reading the LCD in low light conditions.The LCD 416 e can be provided, for example, to provide the user withvisual data output such as is illustrated in FIGS. 3A to 3D. The buttons416 f can provide for toggle, menu selection, option selection, modeselection, and reset, for example.

In some embodiments, prompts or messages can be displayed on the userinterface to convey information to the user, such as reference outliervalues, requests for reference analyte values, therapy recommendations,deviation of the measured analyte values from the estimated analytevalues, and the like. Additionally, prompts can be displayed to guidethe user through calibration or trouble-shooting of the calibration.

Output can be provided via a user interface 416, including but notlimited to, visually on a screen 416 e, audibly through a speaker 416 b,or tactilely through a vibrator 416 c. Additionally, output can beprovided via wired or wireless connection to an external device,including but not limited to, phone, computer, laptop, server, personaldigital assistant, modem connection, insulin delivery mechanism, medicaldevice, or other device that can be useful in interfacing with thereceiver.

Output can be continuously provided, or certain output can beselectively provided based on modes, events, analyte concentrations andthe like. For example, an estimated analyte path can be continuouslyprovided to a patient on an LCD screen 416 e, while audible alerts canbe provided only during a time of existing or approaching clinical riskto a patient. As another example, estimation can be provided based onevent triggers (for example, when an analyte concentration is nearing orentering a clinically risky zone). As yet another example, analyzeddeviation of estimated analyte values can be provided when apredetermined level of variation (for example, due to known error orclinical risk) is known.

In some embodiments, alarms prompt or alert a patient when a measured orprojected analyte value or rate of change simply passes a predeterminedthreshold. In some embodiments, the clinical risk alarms combineintelligent and dynamic estimative algorithms to provide greateraccuracy, more timeliness in pending danger, avoidance of false alarms,and less annoyance for the patient. For example, clinical risk alarms ofthese embodiments include dynamic and intelligent estimative algorithmsbased on analyte value, rate of change, acceleration, clinical risk,statistical probabilities, known physiological constraints, and/orindividual physiological patterns, thereby providing more appropriate,clinically safe, and patient-friendly alarms.

In some embodiments, at least one of a hypoglycemia, hyperglycemia,predicted hypoglycemia, and predicted hyperglycemia alarm includes firstand second user selectable alarms. In some embodiments, the first alarmis configured to alarm during a first time of day and wherein the secondalarm is configured to alarm during a second time of day (for example,so that a host can set different alarm settings for day vs. night,avoiding unnecessary night-time alarming). In some embodiments, thealarm is configured to turn on a light. In some embodiments, the alarmis configured to alarm a remote receiver located more than about 10 feetaway from the continuous glucose sensor (for example, in a parent'sbedroom or to a health care provider). In some embodiments, the alarmcomprises a text message, and wherein the computer system is configuredto send the text message to a remote device. Accordingly, alarms andother system processing can be set by modes of the system, such asdescribed in more detail elsewhere herein.

In some embodiments, clinical risk alarms can be activated for apredetermined time period to allow for the user to attend to his/hercondition. Additionally, the clinical risk alarms can be de-activatedwhen leaving a clinical risk zone so as not to annoy the patient byrepeated clinical risk alarms, when the patient's condition isimproving.

In some embodiments, the system determines a possibility of the patientavoiding clinical risk, based on the analyte concentration, the rate ofchange, and other aspects of the sensor algorithms. If there is minimalor no possibility of avoiding the clinical risk, a clinical risk alarmwill be triggered. However, if there is a possibility of avoiding theclinical risk, the system can wait a predetermined amount of time andre-analyze the possibility of avoiding the clinical risk. In someembodiments, when there is a possibility of avoiding the clinical risk,the system will further provide targets, therapy recommendations, orother information that can aid the patient in proactively avoiding theclinical risk.

In some embodiments, a variety of different display methods are used,such as described in the preferred embodiments, which can be toggledthrough or selectively displayed to the user based on conditions or byselecting a button, for example. As one example, a simple screen can benormally shown that provides an overview of analyte data, for examplepresent analyte value and directional trend. More complex screens canthen be selected when a user desires more detailed information, forexample, historical analyte data, alarms, clinical risk zones, and thelike.

In some embodiments, electronics 422 associated with a medicamentdelivery device 502 are operably connected to the processor 406 andinclude a processor 424 for processing data associated with the deliverydevice 502 and include at least a wired or wireless connection (forexample, RF transceiver) 426 for transmission of data between theprocessor 406 of the receiver 300 and the processor 424 of the deliverydevice 502. Other electronics associated with any of the deliverydevices cited herein, or other known delivery devices, may beimplemented with the delivery device electronics 422 described herein,as is appreciated by one skilled in the art. In some embodiments, type,amount, validation and other processing related to medicament deliveryis based at least in part on a mode of the system, which is described inmore detail elsewhere herein.

In some embodiments, the processor 424 comprises programming forprocessing the delivery information in combination with the continuoussensor information. In some alternative embodiments, the processor 406comprises programming for processing the delivery information incombination with the continuous sensor information. In some embodiments,both processors 406 and 422 mutually process information related to eachcomponent.

In some embodiments, the medicament delivery device 502 further includesa user interface (not shown), which may include a display and/orbuttons, for example. U.S. Pat. Nos. 6,192,891, 5,536,249, and 6,471,689describe some examples of incorporation of a user interface into amedicament delivery device, as is appreciated by one skilled in the art.

In some embodiments, electronics 428 associated with the single pointglucose monitor 428 are operably connected to a processor 432 andinclude a potentiostat 430 in one embodiment that measures a currentflow produced at the working electrode when a biological sample isplaced on a sensing membrane, such as described above. The single pointglucose monitor 428 can include at least one of a wired and a wirelessconnection 434.

FIG. 4B is an illustration of the receiver in one embodiment showing ananalyte trend graph, including measured analyte values, estimatedanalyte values, and a clinical risk zone. The receiver 300 includes anLCD screen 304, buttons 302, and a speaker 416 d and/or microphone. Thescreen 304 displays a trend graph in the form of a line representing thehistorical trend of a patient's analyte concentration. Although axes mayor may not be shown on the screen 304, it is understood that atheoretical x-axis represents time and a theoretical y-axis representsanalyte concentration.

In some embodiments such as shown in FIG. 4B, the screen showsthresholds, including a high threshold 440 and a low threshold 442,which represent boundaries between clinically safe and clinically riskyconditions for the patients. In one exemplary embodiment, a normalglucose threshold for a glucose sensor is set between about 100 and 160mg/dL, and the clinical risk zones 444 are illustrated outside of thesethresholds. In alternative embodiments, the normal glucose threshold isbetween about 80 and about 200 mg/dL, between about 55 and about 220mg/dL, or other threshold that can be set by the manufacturer,physician, patient, computer program, and the like. Although a fewexamples of glucose thresholds are given for a glucose sensor, thesetting of any analyte threshold is not limited by the preferredembodiments, including rate of change and/or acceleration information.In some embodiments, one or more criteria that define clinical riskand/or alarms are based at least in part on a mode of the system, whichis described in more detail elsewhere herein.

In some embodiments, the screen 304 shows clinical risk zones 444, alsoreferred to as danger zones, through shading, gradients, or othergraphical illustrations that indicate areas of increasing clinical risk.Clinical risk zones 444 can be set by a manufacturer, customized by adoctor, and/or set by a user via buttons 302, for example. In someembodiments, the danger zone 444 can be continuously shown on the screen304, or the danger zone can appear when the measured and/or estimatedanalyte values fall into the danger zone 444. Additional information canbe displayed on the screen, such as an estimated time to clinical risk.In some embodiments, the danger zone can be divided into levels ofdanger (for example, low, medium, and high) and/or can be color-coded(for example, yellow, orange, and red) or otherwise illustrated toindicate the level of danger to the patient. Additionally, the screen orportion of the screen can dynamically change colors or illustrationsthat represent a nearness to the clinical risk and/or a severity ofclinical risk.

In some embodiments, such as shown in FIG. 4B, the screen 304 displays atrend graph of measured analyte data 446. Measured analyte data can besmoothed and calibrated such as described in more detail elsewhereherein. Measured analyte data can be displayed for a certain time period(for example, previous 1 hour, 3 hours, 9 hours, etc.) In someembodiments, the user can toggle through screens using buttons 302 toview the measured analyte data for different time periods, usingdifferent formats, or to view certain analyte values (for example, highsand lows).

In some embodiments such as shown in FIG. 4B, the screen 304 displaysestimated analyte data 448 using dots. In this illustration, the size ofthe dots can represent the confidence of the estimation, a variation ofestimated values, and the like. For example, as the time gets fartheraway from the present (t=0) the confidence level in the accuracy of theestimation can decline as is appreciated by one skilled in the art. Insome alternative embodiments, dashed lines, symbols, icons, and the likecan be used to represent the estimated analyte values. In somealternative embodiments, shaded regions, colors, patterns, and the likecan also be used to represent the estimated analyte values, a confidencein those values, and/or a variation of those values, such as describedin more detail in preferred embodiments.

Axes, including time and analyte concentration values, can be providedon the screen, however are not required. While not wishing to be boundby theory, it is believed that trend information, thresholds, and dangerzones provide sufficient information to represent analyte concentrationand clinically educate the user. In some embodiments, time can berepresented by symbols, such as a sun and moon to represent day andnight. In some embodiments, the present or most recent measured analyteconcentration, from the continuous sensor and/or from the referenceanalyte monitor can be continually, intermittently, or selectivelydisplayed on the screen.

The estimated analyte values 448 of FIG. 4B include a portion, whichextends into the danger zone 444. By providing data in a format thatemphasizes the possibility of clinical risk to the patient, appropriateaction can be taken by the user (for example, patient, or caretaker) andclinical risk can be preempted.

FIG. 4C is an illustration of the receiver in another embodiment showinga representation of analyte concentration and directional trend using agradient bar. In this embodiment, the screen illustrates the measuredanalyte values and estimated analyte values in a simple but effectivemanner that communicates valuable analyte information to the user.

In this embodiment, a gradient bar 450 is provided that includesthresholds 452 set at high and lows such as described in more detailwith reference to FIG. 4B, above. Additionally, colors, shading, orother graphical illustration can be present to represent danger zones454 on the gradient bar 450 such as described in more detail withreference to FIG. 4B, above.

The measured analyte value is represented on the gradient bar 450 by amarker 456, such as a darkened or colored bar. By representing themeasured analyte value with a bar 456, a low-resolution analyte value ispresented to the user (for example, within a range of values). Forexample, each segment on the gradient bar 450 can represent about 10mg/dL of glucose concentration. As another example, each segment candynamically represent the range of values that fall within the “A” and“B” regions of the Clarke Error Grid. While not wishing to be bound bytheory, it is believed that inaccuracies known both in reference analytemonitors and/or continuous analyte sensors are likely due to knownvariables such as described in more detail elsewhere herein, and can bede-emphasized such that a user focuses on proactive care of thecondition, rather than inconsequential discrepancies within and betweenreference analyte monitors and continuous analyte sensors.

Additionally, the representative gradient bar communicates thedirectional trend of the analyte concentration to the user in a simpleand effective manner, namely by a directional arrow 458. For example, inconventional diabetic blood glucose monitoring, a person with diabetesobtains a blood sample and measures the glucose concentration using atest strip, and the like. Unfortunately, this information does not tellthe person with diabetes whether the blood glucose concentration isrising or falling. Rising or falling directional trend information canbe particularly important in a situation such as illustrated in FIG. 4C,wherein if the user does not know that the glucose concentration isrising, he/she may assume that the glucose concentration is falling andnot attend to his/her condition. However, because rising directionaltrend information 458 is provided, the person with diabetes can preemptthe clinical risk by attending to his/her condition (for example,administer insulin). Estimated analyte data can be incorporated into thedirectional trend information by characteristics of the arrow, forexample, size, color, flash speed, and the like.

In some embodiments, the gradient bar can be a vertical instead ofhorizontal bar. In some embodiments, a gradient fill can be used torepresent analyte concentration, variation, or clinical risk, forexample. In some embodiments, the bar graph includes color, for examplethe center can be green in the safe zone that graduates to red in thedanger zones; this can be in addition to or in place of the dividedsegments. In some embodiments, the segments of the bar graph are clearlydivided by lines; however color, gradation, and the like can be used torepresent areas of the bar graph. In some embodiments, the directionalarrow can be represented by a cascading level of arrows to a representslow or rapid rate of change. In some embodiments, the directional arrowcan be flashing to represent movement or pending danger.

The screen 304 of FIG. 4C can further comprise a numericalrepresentation of analyte concentration, date, time, or otherinformation to be communicated to the patient. However, a user canadvantageously extrapolate information helpful for his/her conditionusing the simple and effective representation of this embodiment shownin FIG. 4C, without reading a numeric representation of his/her analyteconcentration.

In some alternative embodiments, a trend graph or gradient bar, a dial,pie chart, or other visual representation can provide analyte data usingshading, colors, patterns, icons, animation, and the like.

FIG. 4D is an illustration of a receiver 300 in another embodiment,including a screen 304 that shows a numerical representation of the mostrecent measured analyte value 460. This numerical value 460 ispreferably a calibrated analyte value, such as described in more detailwith reference to FIGS. 5 and 6. Additionally, this embodimentpreferably provides an arrow 462 on the screen 304, which represents therate of change of the host's analyte concentration. A bold “up” arrow isshown on the drawing, which preferably represents a relatively quicklyincreasing rate of change. The arrows shown with dotted lines illustrateexamples of other directional arrows (for example, rotated by 45degrees), which can be useful on the screen to represent various otherpositive and negative rates of change. Although the directional arrowsshown have a relative low resolution (45 degrees of accuracy), otherarrows can be rotated with a high resolution of accuracy (for exampleone degree of accuracy) to more accurately represent the rate of changeof the host's analyte concentration (e.g., the amplitude and/ordirection of the rate of change). In some alternative embodiments, thescreen provides an indication of the acceleration of the host's analyteconcentration.

A second numerical value 464 is shown, which is representative of avariation of the measured analyte value 460. The second numerical valueis preferably determined from a variation analysis based on statistical,clinical, or physiological parameters, such as described in more detailelsewhere herein. In one embodiment, the second numerical value 464 isdetermined based on clinical risk (for example, weighted for thegreatest possible clinical risk to a patient). In another embodiment,the second numerical representation 464 is an estimated analyte valueextrapolated to compensate for a time lag, such as described in moredetail elsewhere herein. In some alternative embodiments, the receiverdisplays a range of numerical analyte values that best represents thehost's estimated analyte value (for example, +/−10%). In someembodiments, the range is weighted based on clinical risk to thepatient. In some embodiments, the range is representative of aconfidence in the estimated analyte value and/or a variation of thosevalues. In some embodiments, the range is adjustable.

Referring again to FIG. 4A, communication ports, including a PCcommunication (com) port 418 and a reference glucose monitor corn port420 can be provided to enable communication with systems that areseparate from, or integral with, the receiver 300. The PC corn port 418,for example, a serial communications port, allows for communicating withanother computer system (e.g., PC, PDA, server, and the like). In oneexemplary embodiment, the receiver 300 is able to download historicaldata to a physician's PC for retrospective analysis by the physician.The reference glucose monitor corn port 420 allows for communicatingwith a reference glucose monitor (not shown) so that reference glucosevalues can be downloaded into the receiver 300, for example,automatically. In one embodiment, the reference glucose monitor isintegral with the receiver 300, and the reference glucose corn port 420allows internal communication between the two integral systems. Inanother embodiment, the reference glucose monitor corn port 420 allows awireless or wired connection to reference glucose monitor such as aself-monitoring blood glucose monitor (e.g., for measuring finger stickblood samples).

Integrated System

Referring now to FIG. 5, in some embodiments, the receiver 300 isintegrally formed with at least one of a medicament delivery device 502,and a single point glucose monitor 504. In some embodiments, thereceiver 300, medicament delivery device 502 and/or single point glucosemonitor 504 are detachably connected, so that one or more of thecomponents can be individually detached and attached at the user'sconvenience. In some embodiments, the receiver 300, medicament deliverydevice 502, and/or single point glucose monitor 504 are separate from,detachably connectable to, or integral with each other; and one or moreof the components are operably connected through a wired or wirelessconnection, allowing data transfer and thus integration between thecomponents. In some embodiments, one or more of the components areoperably linked as described above, while another one or more components(for example, the syringe or patch) are provided as a physical part ofthe system for convenience to the user and as a reminder to enter datafor manual integration of the component with the system. Each of thecomponents of the integrated system 500 may be manually,semi-automatically, or automatically integrated with each other, andeach component may be in physical and/or data communication with anothercomponent, which may include wireless connection, wired connection (forexample, via cables or electrical contacts), or the like. Additionaldescription of integrated systems can be found in U.S. PatentPublication 2005/0192557, entitled “INTEGRATED DELIVERY DEVICE FORCONTINUOUS GLUCOSE SENSOR,” which is incorporated herein by reference inits entirety.

The preferred embodiments provide an integrated system 500, whichincludes a medicament delivery device 502 for administering a medicamentto the patient 501. The integrated medicament delivery device can bedesigned for bolus injection, continuous injection, inhalation,transdermal absorption, other method for administering medicament, orany combinations thereof The term medicament includes any substance usedin therapy for a patient using the system 500, for example, insulin,glucagon, or derivatives thereof Published International Application WO02/43566 describes glucose, glucagon, and vitamins A, C, or D that maybe used with the preferred embodiments. U.S. Pat. Nos. 6,051,551 and6,024,090 describe types of insulin suitable for inhalation that may beused with the preferred embodiments. U.S. Pat. No. 5,234,906, U.S. Pat.No. 6,319,893, and EP 760677 describe various derivatives of glucagonthat may be used with the preferred embodiments. U.S. Pat. No. 6,653,332describes a combination therapy that may be used with the preferredembodiments. U.S. Pat. No. 6,471,689 and WO 81/01794 describe insulinuseful for delivery pumps that may be used with the preferredembodiments. U.S. Pat. No. 5,226,895 describes a method of providingmore than one type of insulin that may be used with the preferredembodiments. All of the above references are incorporated herein byreference in their entirety and may be useful as the medicament(s) inthe preferred embodiments.

A single point glucose monitor 504 includes a meter for measuringglucose within a biological sample including a sensing region that has asensing membrane impregnated with an enzyme, similar to the sensingmembrane described with reference to U.S. Pat. Nos. 4,994,167 and4,757,022, which are incorporated herein in their entirety by reference.However, in alternative embodiments, the single point glucose monitor504 can use other measurement techniques such as optical, for example.It is noted that the meter is optional in that a separate meter can beused and the glucose data downloaded or input by a user into thereceiver.

Calibration

Reference is now made to FIG. 6A, which is a flow chart 600 thatillustrates the process of calibration and data output of the glucosesensor (e.g., 100A or 100B) in one embodiment.

Calibration of the glucose sensor comprises data processing thatconverts a sensor data stream into an estimated glucose measurement thatis meaningful to a user. In some embodiments, a reference glucose valuecan be used to calibrate the data stream from the glucose sensor. In oneembodiment, the analyte sensor is a continuous glucose sensor and one ormore reference glucose values are used to calibrate the data stream fromthe sensor. At initialization of a sensor, “initial calibration” isperformed wherein the sensor is initially calibrated. In someembodiments, during sensor use, “update calibration” is performed toupdate the calibration of the sensor. In some embodiments,“recalibration” is performed to either reinitialize the calibration orperform an update calibration, for example, when the sensor hasdetermined that the previous calibration is no longer valid. Thecalibration can be performed on a real-time basis and/or retrospectivelyrecalibrated. However in alternative embodiments, other calibrationtechniques can be utilized, for example using another constant analyte(for example, folic acid, ascorbate, urate, and the like) as a baseline,factory calibration, periodic clinical calibration, oxygen calibration(for example, using a plurality of sensor heads), and the like can beused.

At block 602, a sensor data receiving module, also referred to as thesensor data module, or processor module, receives sensor data (e.g., adata stream), including one or more time-spaced sensor data pointshereinafter referred to as “data stream,” “sensor data,” “sensor analytedata”, or “glucose signal,” from a sensor via the receiver, which can bein wired or wireless communication with the sensor. The sensor data canbe raw or smoothed (filtered), or include both raw and smoothed data. Insome embodiments, raw sensor data may include an integrated digital datavalue, e.g., a value averaged over a time period such as by a chargecapacitor. Smoothed sensor data point(s) can be filtered in certainembodiments using a filter, for example, a finite impulse response (FIR)or infinite impulse response (IIR) filter. Some or all of the sensordata point(s) can be replaced by estimated signal values to addresssignal noise such as described in more detail elsewhere herein. It isnoted that during the initialization of the sensor, prior to initialcalibration, the receiver 300 (e.g., computer system) receives andstores the sensor data, however it may not display any data to the useruntil initial calibration and eventually stabilization of the sensor hasbeen determined. In some embodiments, the data stream can be evaluatedto determine sensor break-in (equilibrium of the sensor in vitro or invivo).

At block 604, a reference data receiving module, also referred to as thereference input module, or the processor module, receives reference datafrom a reference glucose monitor, including one or more reference datapoints. In one embodiment, the reference glucose points can compriseresults from a self-monitored blood glucose test (e.g., from a fingerstick test). In one such embodiment, the user can administer aself-monitored blood glucose test to obtain a glucose value (e.g.,point) using any known glucose sensor, and enter the numeric glucosevalue into the computer system. In another such embodiment, aself-monitored blood glucose test comprises a wired or wirelessconnection to the receiver 300 (e.g. computer system) so that the usersimply initiates a connection between the two devices, and the referenceglucose data is passed or downloaded between the self-monitored bloodglucose test and the receiver 300. In yet another such embodiment, theself-monitored glucose test is integral with the receiver 300 so thatthe user simply provides a blood sample to the receiver 300, and thereceiver 300 runs the glucose test to determine a reference glucosevalue. Co-pending U.S. patent application Ser. No. 10/991,966 filed onNov. 17, 2004 and entitled “INTEGRATED RECEIVER FOR CONTINUOUS ANALYTESENSOR” describes some systems and methods for integrating a referenceanalyte monitor into a receiver for a continuous analyte sensor.

In some alternative embodiments, the reference data is based on sensordata from another substantially continuous analyte sensor, e.g. atranscutaneous analyte sensor or another type of suitable continuousanalyte sensor. In an embodiment employing a series of two or moretranscutaneous (or other continuous) sensors, the sensors can beemployed so that they provide sensor data in discrete or overlappingperiods. In such embodiments, the sensor data from one continuous sensorcan be used to calibrate another continuous sensor, or be used toconfirm the validity of a subsequently employed continuous sensor.

In some embodiments, the calibration process 600 monitors the continuousanalyte sensor data stream to determine a preferred time for capturingreference analyte concentration values for calibration of the continuoussensor data stream. In an example wherein the analyte sensor is acontinuous glucose sensor, when data (for example, observed from thedata stream) changes too rapidly, the reference glucose value may not besufficiently reliable for calibration due to unstable glucose changes inthe host. In contrast, when sensor glucose data are relatively stable(for example, relatively low rate of change), a reference glucose valuecan be taken for a reliable calibration. In one embodiment, thecalibration process 600 can prompt the user via the user interface to“calibrate now” when the analyte sensor is considered stable.

In some embodiments, the calibration process 600 can prompt the user viathe user interface 416 to obtain a reference analyte value forcalibration at intervals, for example when analyte concentrations are athigh and/or low values. In some additional embodiments, the userinterface 416 can prompt the user to obtain a reference analyte valuefor calibration based upon certain events, such as meals, exercise,large excursions in analyte levels, faulty or interrupted data readings,and the like. In some embodiments, the estimative algorithms can provideinformation useful in determining when to request a reference analytevalue. For example, when estimated analyte values indicate approachingclinical risk, the user interface 416 can prompt the user to obtain areference analyte value.

Certain acceptability parameters can be set for reference valuesreceived from the user. For example, in one embodiment, the receiver mayonly accept reference glucose values between about 40 and about 400mg/dL.

In some embodiments, the calibration process 600 performs outlierdetection on the reference data and time corresponding sensor data.Outlier detection compares a reference analyte value with a timecorresponding measured analyte value to ensure a predeterminedstatistically, physiologically, or clinically acceptable correlationbetween the corresponding data exists. In an example wherein the analytesensor is a glucose sensor, the reference glucose data is matched withsubstantially time corresponding calibrated sensor data and the matcheddata are plotted on a Clarke Error Grid to determine whether thereference analyte value is an outlier based on clinical acceptability,such as described in more detail with reference U.S. Patent PublicationNo. US-2005-0027463-A1. In some embodiments, outlier detection comparesa reference analyte value with a corresponding estimated analyte value,such as described in more detail elsewhere herein and with reference tothe above-described patent application, and the matched data isevaluated using statistical, clinical, and/or physiological parametersto determine the acceptability of the matched data pair. In alternativeembodiments, outlier detection can be determined by other clinical,statistical, and/or physiological boundaries.

In some embodiments, outlier detection utilizes signal artifactsdetection, described in more detail elsewhere herein, to determine thereliability of the reference data and/or sensor data responsive to theresults of the signal artifacts detection. For example, if a certainlevel of signal artifacts is not detected in the data signal, then thesensor data is determined to be reliable. As another example, if acertain level of signal artifacts is detected in the data signal, thenthe reference glucose data is determined to be reliable.

The reference data can be pre-screened according to environmental andphysiological issues, such as time of day, oxygen concentration,postural effects, and patient-entered environmental data. In oneexemplary embodiment, wherein the sensor comprises an implantableglucose sensor, an oxygen sensor within the glucose sensor is used todetermine if sufficient oxygen is being provided to successfullycomplete the necessary enzyme and electrochemical reactions for accurateglucose sensing. In another exemplary embodiment, the patient isprompted to enter data into the user interface, such as meal timesand/or amount of exercise, which can be used to determine likelihood ofacceptable reference data. In yet another exemplary embodiment, thereference data is matched with time-corresponding sensor data, which isthen evaluated on a modified clinical error grid to determine itsclinical acceptability.

Some evaluation data, such as described in the paragraph above, can beused to evaluate an optimum time for reference analyte measurement, suchas described in more detail with reference to FIG. 7. Correspondingly,the user interface can then prompt the user to provide a reference datapoint for calibration within a given time period. Consequently, becausethe receiver proactively prompts the user during optimum calibrationtimes, the likelihood of error due to environmental and physiologicallimitations can decrease and consistency and acceptability of thecalibration can increase.

At block 606, a data matching module, also referred to as the processormodule, matches reference data (e.g., one or more reference glucose datapoints) with substantially time corresponding sensor data (e.g., one ormore sensor data points) to provide one or more matched data pairs. Inone embodiment, one reference data point is matched to one timecorresponding sensor data point to form a matched data pair. In anotherembodiment, a plurality of reference data points are averaged (e.g.,equally or non-equally weighted average, mean-value, median, and thelike) and matched to one time corresponding sensor data point to form amatched data pair. In another embodiment, one reference data point ismatched to a plurality of time corresponding sensor data points averagedto form a matched data pair. In yet another embodiment, a plurality ofreference data points are averaged and matched to a plurality of timecorresponding sensor data points averaged to form a matched data pair.

In one embodiment, a time corresponding sensor data comprises one ormore sensor data points that occur, for example, 15±5 min after thereference glucose data timestamp (e.g., the time that the referenceglucose data is obtained). In this embodiment, the 15 minute time delayhas been chosen to account for an approximately 10 minute delayintroduced by the filter used in data smoothing and an approximately 5minute diffusional time-lag (e.g., the time necessary for the glucose todiffusion through a membrane(s) of a glucose sensor). In alternativeembodiments, the time corresponding sensor value can be more or lessthan in the above-described embodiment, for example ±60 minutes.Variability in time correspondence of sensor and reference data can beattributed to, for example, a longer or shorter time delay introducedduring signal estimation, or if the configuration of the glucose sensorincurs a greater or lesser physiological time lag.

In another embodiment, time corresponding sensor data comprises one ormore sensor data points that occur from about 0 minutes to about 20minutes after the reference analyte data time stamp (e.g. the time thatthe reference analyte data is obtained). In one embodiment, a 5-minutetime delay is chosen to compensate for a system time-lag (e.g. the timenecessary for the analyte to diffusion through a membrane(s) of ananalyte sensor). In alternative embodiments, the time correspondingsensor value can be earlier than or later than that of theabove-described embodiment, for example ±60 minutes. Variability in timecorrespondence of sensor and reference data can be attributed to, forexample, a longer or shorter time delay introduced by the data smoothingfilter, or if the configuration of the analyte sensor incurs a greateror lesser physiological time lag.

In some practical implementations of the sensor, the reference glucosedata can be obtained at a time that is different from the time that thedata is input into the receiver 300. Accordingly, it should be notedthat the “time stamp” of the reference glucose (e.g., the time at whichthe reference glucose value was obtained) may not be the same as thetime at which the receiver 300 obtained the reference glucose data.Therefore, some embodiments include a time stamp requirement thatensures that the receiver 300 stores the accurate time stamp for eachreference glucose value, that is, the time at which the reference valuewas actually obtained from the user.

In some embodiments, tests are used to evaluate the best-matched pairusing a reference data point against individual sensor values over apredetermined time period (e.g., about 30 minutes). In one suchembodiment, the reference data point is matched with sensor data pointsat 5-minute intervals and each matched pair is evaluated. The matchedpair with the best correlation can be selected as the matched pair fordata processing. In some alternative embodiments, matching a referencedata point with an average of a plurality of sensor data points over apredetermined time period can be used to form a matched pair.

In some embodiments wherein the data signal is evaluated for signalartifacts, as described in more detail elsewhere herein, the processormodule is configured to form a matched data pair only if a signalartifact is not detected. In some embodiments wherein the data signal isevaluated for signal artifacts, the processor module is configured toprompt a user for a reference glucose value during a time when one ormore signal artifact(s) is not detected.

At block 608, a calibration set module, also referred to as theprocessor module, forms an initial calibration set from a set of one ormore matched data pairs, which are used to determine the relationshipbetween the reference glucose data and the sensor glucose data, such asdescribed in more detail with reference to block 610, below.

The matched data pairs, which make up the initial calibration set, canbe selected according to predetermined criteria. In some embodiments,the number (n) of data pair(s) selected for the initial calibration setis one. In other embodiments, n data pairs are selected for the initialcalibration set wherein n is a function of the frequency of the receivedreference data points. In one exemplary embodiment, six data pairs makeup the initial calibration set. In another embodiment, the calibrationset includes only one data pair. In an embodiment wherein asubstantially continuous analyte sensor provides reference data,numerous data points are used to provide reference data from more than 6data pairs (e.g., dozens or even hundreds of data pairs). In oneexemplary embodiment, a substantially continuous analyte sensor provides288 reference data points per day (every five minutes for twenty-fourhours), thereby providing an opportunity for a matched data pair 288times per day, for example. While specific numbers of matched data pairsare referred to in the preferred embodiments, any suitable number ofmatched data pairs per a given time period can be employed.

In some embodiments, the data pairs are selected only within a certainglucose value threshold, for example wherein the reference glucose valueis between about 40 and about 400 mg/dL. In some embodiments, the datapairs that form the initial calibration set are selected according totheir time stamp. In certain embodiments, the data pairs that form theinitial calibration set are selected according to their time stamp, forexample, by waiting a predetermined “break-in” time period afterimplantation, the stability of the sensor data can be increased. Incertain embodiments, the data pairs that form the initial calibrationset are spread out over a predetermined time period, for example, aperiod of two hours or more. In certain embodiments, the data pairs thatform the initial calibration set are spread out over a predeterminedglucose range, for example, spread out over a range of at least 90 mg/dLor more.

In some embodiments, wherein the data signal is evaluated for signalartifacts, as described in more detail elsewhere herein, the processormodule is configured to utilize the reference data for calibration ofthe glucose sensor only if a signal artifact is not detected.

At block 610, the conversion function module, also referred to as theprocessor module, uses the calibration set to create a conversionfunction. The conversion function substantially defines the relationshipbetween the reference glucose data and the glucose sensor data. Avariety of known methods can be used with the preferred embodiments tocreate the conversion function from the calibration set. In oneembodiment, wherein a plurality of matched data points form the initialcalibration set, a linear least squares regression is performed on theinitial calibration set such as described in more detail with referenceto FIG. 6B.

At block 612, a sensor data transformation module, also referred to asthe processor module, uses the conversion function to transform sensordata into substantially real-time glucose value estimates, also referredto as calibrated data, or converted sensor data, as sensor data iscontinuously (or intermittently) received from the sensor. For example,the sensor data, which can be provided to the receiver in “counts,” istranslated in to estimate analyte value(s) in mg/dL. In other words, theoffset value at any given point in time can be subtracted from the rawvalue (e.g., in counts) and divided by the slope to obtain the estimatedglucose value:

${{mg}/{dL}} = \frac{\left( {{rawvalue} - {offset}} \right)}{slope}$

In some alternative embodiments, the sensor and/or reference glucosevalues are stored in a database for retrospective analysis.

At block 614, an output module, also referred to as the processormodule, provides output to the user via the user interface. The outputis representative of the estimated glucose value, which is determined byconverting the sensor data into a meaningful glucose value such asdescribed in more detail with reference to block 612, above. User outputcan be in the form of a numeric estimated glucose value, an indicationof directional trend of glucose concentration, and/or a graphicalrepresentation of the estimated glucose data over a period of time, forexample. Other representations of the estimated glucose values are alsopossible, for example audio and tactile.

In one embodiment, such as shown in FIG. 3A, the estimated glucose valueis represented by a numeric value. In other exemplary embodiments, suchas shown in FIGS. 3B to 3D, the user interface graphically representsthe estimated glucose data trend over predetermined a time period (e.g.,one, three, and nine hours, respectively). In alternative embodiments,other time periods can be represented. In alternative embodiments,pictures, animation, charts, graphs, ranges of values, and numeric datacan be selectively displayed.

Accordingly, after initial calibration of the sensor, real-timecontinuous glucose information can be displayed on the user interface sothat the user can regularly and proactively care for his/her diabeticcondition within the bounds set by his/her physician.

In alternative embodiments, the conversion function is used to predictglucose values at future points in time. These predicted values can beused to alert the user of upcoming hypoglycemic or hyperglycemic events.Additionally, predicted values can be used to compensate for a time lag(e.g., 15 minute time lag such as described elsewhere herein), if any,so that an estimated glucose value displayed to the user represents theinstant time, rather than a time delayed estimated value.

In some embodiments, the substantially real-time estimated glucosevalue, a predicted future estimated glucose value, a rate of change,and/or a directional trend of the glucose concentration is used tocontrol the administration of a constituent to the user, including anappropriate amount and time, in order to control an aspect of the user'sbiological system. One such example is a closed loop glucose sensor andinsulin pump, wherein the glucose data (e.g., estimated glucose value,rate of change, and/or directional trend) from the glucose sensor isused to determine the amount of insulin, and time of administration,that can be given to a diabetic user to evade hyper- and hypoglycemicconditions.

FIG. 6B is a graph that illustrates one embodiment of a regressionperformed on a calibration set to create a conversion function such asdescribed with reference to FIG. 6A, block 610, above. In thisembodiment, a linear least squares regression is performed on theinitial calibration set. The x-axis represents reference glucose data;the y-axis represents sensor data. The graph pictorially illustratesregression of matched pairs 616 in the calibration set. The regressioncalculates a slope 618 and an offset 620, for example, using thewell-known slope-intercept equation (y=mx+b), which defines theconversion function.

In alternative embodiments, other algorithms could be used to determinethe conversion function, for example forms of linear and non-linearregression, for example fuzzy logic, neural networks, piece-wise linearregression, polynomial fit, genetic algorithms, and other patternrecognition and signal estimation techniques.

In yet other alternative embodiments, the conversion function cancomprise two or more different optimal conversions because an optimalconversion at any time is dependent on one or more parameters, such astime of day, calories consumed, exercise, or glucose concentration aboveor below a set threshold, for example. In one such exemplary embodiment,the conversion function is adapted for the estimated glucoseconcentration (e.g., high vs. low). For example in an implantableglucose sensor it has been observed that the cells surrounding theimplant will consume at least a small amount of glucose as it diffusestoward the glucose sensor. Assuming the cells consume substantially thesame amount of glucose whether the glucose concentration is low or high,this phenomenon will have a greater effect on the concentration ofglucose during low blood sugar episodes than the effect on theconcentration of glucose during relatively higher blood sugar episodes.Accordingly, the conversion function can be adapted to compensate forthe sensitivity differences in blood sugar level. In one implementation,the conversion function comprises two different regression lines,wherein a first regression line is applied when the estimated bloodglucose concentration is at or below a certain threshold (e.g., 150mg/dL) and a second regression line is applied when the estimated bloodglucose concentration is at or above a certain threshold (e.g., 150mg/dL). In one alternative implementation, a predetermined pivot of theregression line that forms the conversion function can be applied whenthe estimated blood is above or below a set threshold (e.g., 150 mg/dL),wherein the pivot and threshold are determined from a retrospectiveanalysis of the performance of a conversion function and its performanceat a range of glucose concentrations. In another implementation, theregression line that forms the conversion function is pivoted about apoint in order to comply with clinical acceptability standards (e.g.,Clarke Error Grid, Consensus Grid, mean absolute relative difference, orother clinical cost function). Although only a few exampleimplementations are described, other embodiments include numerousimplementations wherein the conversion function is adaptively appliedbased on one or more parameters that can affect the sensitivity of thesensor data over time.

In some other alternative embodiments, the sensor is calibrated with asingle-point through the use of a dual-electrode system to simplifysensor calibration. In one such dual-electrode system, a first electrodefunctions as a hydrogen peroxide sensor including a membrane systemcontaining glucose-oxidase disposed thereon, which operates as describedherein. A second electrode is a hydrogen peroxide sensor that isconfigured similar to the first electrode, but with a modified membranesystem (with the enzyme domain removed, for example). This secondelectrode provides a signal composed mostly of the baseline signal, b.

In some dual-electrode systems, the baseline signal is (electronicallyor digitally) subtracted from the glucose signal to obtain a glucosesignal substantially without baseline. Accordingly, calibration of theresultant difference signal can be performed by solving the equationy=mx with a single paired measurement. Calibration of the implantedsensor in this alternative embodiment can be made less dependent on thevalues/range of the paired measurements, less sensitive to error inmanual blood glucose measurements, and can facilitate the sensor's useas a primary source of glucose information for the user. Co-pending U.S.patent application Ser. No. 11/004,561 filed Dec. 3, 2004 and entitled,“CALIBRATION TECHNIQUES FOR A CONTINUOUS ANALYTE SENSOR” describessystems and methods for subtracting the baseline from a sensor signal.

In some alternative dual-electrode system embodiments, the analytesensor is configured to transmit signals obtained from each electrodeseparately (e.g. without subtraction of the baseline signal). In thisway, the receiver can process these signals to determine additionalinformation about the sensor and/or analyte concentration. For example,by comparing the signals from the first and second electrodes, changesin baseline and/or sensitivity can be detected and/or measured and usedto update calibration (e.g. without the use of a reference analytevalue). In one such example, by monitoring the corresponding first andsecond signals over time, an amount of signal contributed by baselinecan be measured. In another such example, by comparing fluctuations inthe correlating signals over time, changes in sensitivity can bedetected and/or measured.

In some alternative embodiments, a regression equation y=mx+b is used tocalculate the conversion function; however, prior information can beprovided for m and/or b, thereby enabling calibration to occur withfewer paired measurements. In one calibration technique, priorinformation (e.g., obtained from in vivo or in vitro tests) determines asensitivity of the sensor and/or the baseline signal of the sensor byanalyzing sensor data from measurements taken by the sensor (e.g., priorto inserting the sensor). For example, if there exists a predictiverelationship between in vitro sensor parameters and in vivo parameters,then this information can be used by the calibration procedure. Forexample, if a predictive relationship exists between in vitrosensitivity and in vivo sensitivity, m≈f(m_(in vitro)) then thepredicted m can be used, along with a single matched pair, to solve forb (b=y−mx). If, in addition, b can be assumed =0, for example with adual-electrode configuration that enables subtraction of the baselinefrom the signal such as described above, then both m and b are known apriori, matched pairs are not needed for calibration, and the sensor canbe completely calibrated e.g. without the need for reference analytevalues (e.g. values obtained after implantation in vivo.)

In another alternative embodiment, prior information can be provided toguide or validate the baseline (b) and/or sensitivity (m) determinedfrom the regression analysis. In this embodiment, boundaries can be setfor the regression line that defines the conversion function such thatworking sensors are calibrated accurately and easily (with two points),and non-working sensors are prevented from being calibrated. If theboundaries are drawn too tightly, a working sensor may not enter intocalibration. Likewise, if the boundaries are drawn too loosely, thescheme can result in inaccurate calibration or can permit non-workingsensors to enter into calibration. For example, subsequent to performingregression, the resulting slope and/or baseline are tested to determinewhether they fall within a predetermined acceptable threshold(boundaries). These predetermined acceptable boundaries can be obtainedfrom in vivo or in vitro tests (e.g. by a retrospective analysis ofsensor sensitivities and/or baselines collected from a set ofsensors/patients, assuming that the set is representative of futuredata).

If the slope and/or baseline fall within the predetermined acceptableboundaries, then the regression is considered acceptable and processingcontinues to the next step. Alternatively, if the slope and/or baselinefall outside the predetermined acceptable boundaries, steps can be takento either correct the regression or fail-safe such that a system willnot process or display errant data. This can be useful in situationswherein regression results in errant slope or baseline values. Forexample, when points (matched pairs) used for regression are too closein value, the resulting regression is statistically less accurate thanwhen the values are spread farther apart. As another example, a sensorthat is not properly deployed or is damaged during deployment can yielda skewed or errant baseline signal.

Reference is now made to FIG. 6C, which is a flow chart 630 thatillustrates the process of immediate calibration of a continuous analytesensor in one embodiment.

In conventional analyte sensors, during initial calibration, updatecalibration and/or recalibration of a sensor system, a user must wait apredetermined period of time (e.g., at least about 5, 10, 15, 20, 25 ormore minutes after entry of a reference analyte value for the system tooutput (e.g., display) its first calibrated analyte measurement,resulting in an inconvenience to the user including a time delayedresponse of calibrated sensor data and/or whether or not the referenceanalyte value was accepted for calibration.

The preferred embodiments provide systems and methods to improve theresponsiveness of a user interface (e.g., data output) to receivedreference analyte values (e.g., a reading from a blood glucose meter)for faster feedback to the user. Although calibration preferablycompensates for a time lag between the reference analyte values (e.g.,blood glucose meter readings) and glucose sensor readings (e.g.,continuous glucose sensor readings subject to processing such asfiltering), some circumstances exist wherein an immediate calibrationdoes not compensate for a time lag (e.g., prior to receivingtime-corresponding sensor data). In one example, a 5-minute time lag isinduced in continuous sensor data by a filter or integrator of rawcontinuous sensor data (e.g., a signal)).

At block 632, a sensor data receiving module, also referred to as thesensor data module, or processor module, receives sensor data (e.g., adata stream), including one or more time-spaced sensor data pointshereinafter referred to as “data stream,” “sensor data,” “sensor analytedata”, or “glucose signal,” from a sensor via the receiver, which can bein wired or wireless communication with the sensor. The sensor data canbe raw or smoothed (filtered), or include both raw and smoothed data. Insome embodiments, raw sensor data may include an integrated digital datavalue, e.g., a value averaged over a time period such as by a chargecapacitor. Smoothed sensor data point(s) can be filtered in certainembodiments using a filter, for example, a finite impulse response (FIR)or infinite impulse response (IIR) filter. Some or all of the sensordata point(s) can be replaced by estimated signal values to addresssignal noise such as described in more detail elsewhere herein. It isnoted that during the initialization of the sensor, prior to initialcalibration, the receiver 300 (e.g., computer system) receives andstores the sensor data, however it may not display any data to the useruntil initial calibration and eventually stabilization of the sensor hasbeen determined. In some embodiments, the data stream can be evaluatedto determine sensor break-in (equilibrium of the sensor in vitro or invivo).

At block 634, a reference data receiving module, also referred to as thereference input module, or the processor module, receives reference datafrom a reference glucose monitor, including one or more reference datapoints. In one embodiment, the reference glucose points can compriseresults from a self-monitored blood 504 (e.g., from a finger sticktest). In one such embodiment, the user can administer a self-monitoredblood glucose test to obtain a glucose value (e.g., point) using anyknown glucose sensor, and enter the numeric glucose value into thecomputer system. In another such embodiment, a self-monitored bloodglucose test comprises a wired or wireless connection to the receiver300 (e.g. computer system) so that the user simply initiates aconnection between the two devices, and the reference glucose data ispassed or downloaded between the self-monitored blood glucose test andthe receiver 300. In yet another such embodiment, the self-monitoredglucose test is integral with the receiver 300 so that the user simplyprovides a blood sample to the receiver 300, and the receiver 300 runsthe glucose test to determine a reference glucose value. Co-pending U.S.patent application Ser. No. 10/991,966 filed on Nov. 17, 2004 andentitled “INTEGRATED RECEIVER FOR CONTINUOUS ANALYTE SENSOR” describessome systems and methods for integrating a reference analyte monitorinto a receiver for a continuous analyte sensor.

In some alternative embodiments, the reference data is based on sensordata from another substantially continuous analyte sensor, e.g. atranscutaneous analyte sensor or another type of suitable continuousanalyte sensor. In an embodiment employing a series of two or moretranscutaneous (or other continuous) sensors, the sensors can beemployed so that they provide sensor data in discrete or overlappingperiods. In such embodiments, the sensor data from one continuous sensorcan be used to calibrate another continuous sensor, or be used toconfirm the validity of a subsequently employed continuous sensor.

At block 636, an data matching is performed by matching a referenceanalyte value to the closest sensor data point (e.g., prior, estimatedand/or predicted sensor data point), also referred to as an “immediatematch,” such that calibration can be performed and immediate feedbackgiven to the user. Preferably, immediate calibration enables display ofthe one or more estimated analyte values within about 10, 8, 6, 5, 4, 3,2, or 1 minute(s) of receiving the reference analyte value. Preferably,immediate calibration is accomplished by matching data pairsimmediately, for example, without compensating for a time lag betweenthe reference glucose value and the sensor glucose value such that atime stamp of the reference glucose value is as close as possible to atime stamp of the sensor glucose value. In some further embodiments, thetime stamp of the reference glucose value is within about 5 minutes, 2.5minutes, 1 minute or less of the time stamp of the sensor glucose valuein the matched data pair.

In another embodiment, an immediate calibration is performed by matchinga reference analyte value to a projected sensor data point (e.g., usingprediction described herein elsewhere) such that calibration can beperformed and immediate feedback given to the user. The projected valuewill therefore be used to compensate for the time differential betweenobtaining the analyte sensor value and converting this value to onecomparable to the reference analyte value. Preferably, this embodimentof immediate calibration enables display of the one or more estimatedanalyte values within about 10, 8, 6, 5, 4, 3, 2, or 1 minute ofcalculating the projected reference analyte value.

Subsequently, “standard calibration,” is performed, also referred to assubsequent calibration, wherein the reference analyte value can bere-matched to a more optimal sensor data point, such as described inmore detail elsewhere herein with reference to matching data pairs, forexample, when additional sensor data points are obtained. In someembodiments, the subsequent calibration, also referred to as “standardcalibration,” is performed once additional sensor data is obtained andmatched with the receiving reference analyte value as described in moredetail elsewhere herein, for example with reference to the data matchingmodule. In some embodiments, the standard calibration utilizes matcheddata pairs chosen to adjust for a time lag between a reference glucosevalue and a sensor glucose value. In one such example, a time lag isinduced at least in part by a filter applied to raw glucose sensor datameasured by the continuous glucose sensor. In some embodiments, forexample, wherein optimal sensor data for matching with the referenceanalyte data is not available (e.g., due to sensor-receivercommunication problems), the immediate calibration is utilized (e.g.,calibrated data displayed using the immediate match) until one or moreadditional reference analyte values are available for calibration.

In some embodiments, immediate calibration provides a calibration linethat determines, predicts or estimates the calibration state that willbe found with the subsequent calibration (e.g., whether the sensor willbe in-calibration or out-of-calibration responsive to the receivedreference analyte value). Preferably, the immediate calibration providessufficient accuracy such that displayed sensor data during immediatecalibration corresponds to and/or flows with the glucose valuesdisplayed after the standard calibration (e.g., substantially withoutnon-physiological fluctuations in the displayed data). Accordingly,immediate calibration is preferably configured with other processing andfail-safes, as described in more detail elsewhere herein with referenceto calibration (e.g., algorithms such as outlier detection, intelligentselection of other matched data pairs in the calibration set, and thelike). In preferred embodiments, the conversion function provided by theimmediate calibration (e.g., the immediate match calibration line) issimilar to the conversion function provided by the standard calibration(e.g., the subsequent calibration line), for example within about+/−20%.

At block 638, a sensor data transformation module, also referred to asthe processor module, uses a conversion function (described elsewhereherein) to transform sensor data into substantially real-time glucosevalue estimates, also referred to as calibrated data, or convertedsensor data, as sensor data is continuously (or intermittently) receivedfrom the sensor. For example, the sensor data, which can be provided tothe receiver in “counts,” is translated in to estimate analyte value(s)in mg/dL as described in reference to FIG. 6A.

At block 640, an output module, also referred to as the processormodule, provides output to the user via the user interface. The outputis representative of the estimated glucose value, which is determined byconverting the sensor data into a meaningful glucose value such asdescribed in more detail with reference to block 612, above. User outputcan be in the form of a numeric estimated glucose value, an indicationof directional trend of glucose concentration, and/or a graphicalrepresentation of the estimated glucose data over a period of time, forexample. Other representations of the estimated glucose values are alsopossible, for example audio and tactile.

Reference is now made to FIG. 7, which is a flow chart 700 thatillustrates the process of smart or intelligent calibration of acontinuous analyte sensor in one embodiment. In general, conventionalcalibration of analyte sensors can have some inaccuracy, for example,caused by drift of the sensor signal, algorithmic-induced inaccuracies,reference analyte measurement error and signal disagreement (e.g.,between the sensor signal and reference signal). As one example, atemporary signal disagreement, or a transient lack of correlationbetween interstitial analyte sensor data and blood analyte referencedata, can be related to biology, such as differences in interstitial andblood glucose levels. Accordingly, conventional sensors can suffer fromcalibration inaccuracy as a result of temporary signal disagreement, andthe like.

Some conventional continuous analyte sensors request reference data atpredetermined time periods during sensor use (e.g., a sensor session),for example every 12 hours or at certain predetermined regular orirregular intervals. However, because of reasons described above, moreor fewer reference data (analyte values) may be required to calibratethe sensor accurately, which can vary from sensor to sensor and/or hostto host. For example, if a sensor signal exhibits a lot of drift, morereference data may be necessary. If a sensor signal exhibits very littledrift, less reference data may be sufficient for good sensorcalibration. Preferably, the smart calibration 700 as described hereinassociates sensor calibration with sensor performance. Accordingly, theembodiments described herein enable a sensor that avoids or overcomescalibration inaccuracies caused by signal drift, temporary signaldisagreement, and the like.

In some preferred embodiments, systems and methods are provided forcalibration of a continuous glucose sensor, wherein the systemdetermines an amount of drift on the sensor signal over a time periodand requests reference data when the amount of drift is greater than athreshold. Sensor drift can be determined by monitoring a change insignal strength (e.g., using a low pass filter) during a sensor sessionand/or monitoring a change in calibration information (e.g., matcheddata pairs, calibration set and/or calibration line) over a sensorsession, for example.

In some preferred embodiments, systems and methods are provided forcalibration of a continuous glucose sensor, wherein the systemdetermines a predictive accuracy of calibration information and requestsreference data based at least in part on the predictive accuracy of thecalibration information.

At block 710, a sensor data receiving module, also referred to as thesensor data module, or processor module, receives sensor data (e.g., adata stream), including one or more time-spaced sensor data pointshereinafter referred to as “data stream,” “sensor data,” “sensor analytedata”, “signal,” from a sensor via the receiver, which can be in wiredor wireless communication with the sensor. The sensor data receivingmodule is described in more detail elsewhere herein, for example, withreference to FIG. 5.

At block 720, a calibration module, also referred to as the processormodule, receives and processes calibration information. In someembodiments, the calibration module receives reference data from areference analyte monitor (e.g., glucose monitor), including one or morereference data points, which is described in more detail with referenceto the reference data receiving module, for example, with reference toFIG. 5. In general, reference data can be received at sensor start-upand/or periodically or intermittently throughout the sensor session. Itis appreciated by one of ordinary skill in the art that reference datacan be received before, during and/or after receiving sensor data.

In some embodiments, the calibration module, matches reference data(e.g., one or more reference glucose data points) with substantiallytime corresponding sensor data (e.g., one or more sensor data points) toprovide one or more matched data pairs, which is described in moredetail elsewhere herein, for example, with reference to the datamatching module associated with FIG. 6A. In one embodiment, onereference data point is matched to one time corresponding sensor datapoint to form a matched data pair.

In some embodiments, the calibration module, forms a calibration setfrom a set of one or more matched data pairs, which are used todetermine the relationship between the reference analyte (e.g., glucose)data and the sensor analyte (e.g., glucose data), such as described inmore detail with reference to block 730, for example.

At block 730, an evaluation module, also referred to as the processormodule, evaluates a predictive accuracy of the calibration information.The term “predictive accuracy” as used herein is a broad term and is tobe given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and furthermore refers without limitation to a measure of howaccurate or indicative calibration information is to a true correlationbetween the analyte signal and the actual analyte concentration, forexample, a measure of how well a matched data pair, a plurality ofmatched data pairs, a calibration set, and/or a calibration line willaccurately predict (i.e., estimate/correlate) glucose concentration froma sensor signal across a physiologically relevant range of glucoseconcentrations (e.g., between about 30 mg/dL and 600 mg/dL of glucoseconcentration).

In general, the evaluation module evaluates the predictive accuracy byevaluating a correlation (or lack thereof), a discordance (or lackthereof), a goodness of fit (or lack thereof), a leverage (or lackthereof), and/or the like, of sensor performance and/or calibrationinformation to determine a predictive accuracy of the calibration usinga data association function, clinical acceptability, and/or the like.

In some embodiments, the evaluation module evaluates a predictiveaccuracy by determining a correlation of (or lack of correlation of) oneor more matched pairs (e.g., a newly received matched data pair) with anexisting calibration set. For example, the evaluation module canevaluate whether a newly received matched data pair(s) fits within theexisting calibration set or the newly received matched data pair(s)changes the calibration set, such as by evaluating a change in acalibration line (e.g., regression line) formed with and without thenewly received matched data pair(s) included therein.

In some embodiments, after receiving a new matched data pair, theprocessor module forms a new calibration set that includes the newlyreceived matched data pair, and forms a new calibration line from thenew calibration set; subsequently, the evaluation module evaluates apredictive accuracy by evaluating a correlation of the matched datapairs in the (existing) calibration set (e.g., the calibration setwithout the newly received matched data pair) with the new calibrationline (e.g., formed from the new calibration set including the newlyreceived matched data pair).

In some embodiments, after receiving a new matched data pair and forminga new calibration set including the newly received matched data pair,the evaluation module evaluates a predictive accuracy by evaluating adiscordance of the new matched data pair and/or the matched data pairsin the new calibration set. In some embodiments, a new matched pair iscompared against the distribution (e.g., “cloud”) of matched data pairsin the calibration set, whereby a predictive accuracy is determinedbased on a correlation and/or deviation of the new matched data pairrelative to the distribution of matched data pairs in the calibrationset.

In some embodiments, the evaluation module evaluates a predictiveaccuracy by iteratively evaluating a plurality of combinations ofmatched data pairs in the calibration set to obtain a plurality ofcalibration lines; for example, if the calibration set includes 5matched data pairs, the processor module can systematically remove eachof the matched data pairs from the calibration set, one at a time, andevaluate the resulting 4-data pair calibration sets. One skilled in theart appreciates the variety of combinations of matched data pairs in acalibration set that can be evaluated, which is dependent upon thenumber of matched data pairs in the calibration set and the number ofmatched data pairs that are removed during each iteration, all of whichis encompassed herein. In some embodiments, the processor module removesone or more of the matched data pairs from the calibration set inresponse to the iterative evaluation; for example, due to a lack ofcorrelation of and/or a discordance of a calibration set and/orcalibration line, resulting from one or more matched data pairs that donot fit well with other of the matched data pairs in the calibrationset. Advantageously, this embodiment identifies matched data pairs toremove from the calibration set (e.g., due to inaccuracies and/or driftof the sensor signal).

In some embodiments, the evaluation module evaluates a predictiveaccuracy by evaluating a leverage of the reference data based at leastin part on a glucose concentration associated with the reference data.The term “leverage” as used herein is a broad term and is to be givenits ordinary and customary meaning to a person of ordinary skill in theart (and is not to be limited to a special or customized meaning), andfurthermore refers without limitation to a measure of how muchcalibration information increases a predictive accuracy of the sensorcalibration, for example, how much newly received reference dataincreases the accuracy of the calibration across a physiologicallyrelevant range of glucose concentration (e.g., 30 to 600 mg/dL). In someembodiments, the evaluation module evaluates a glucose concentration ofthe reference data to determine its leverage in a calibration set,wherein a glucose concentration that significantly increases the spreadof glucose concentrations represented in a calibration set providesleverage, and wherein a glucose concentration that does notsignificantly increase the spread of glucose concentration representedin the existing calibration provides more redundancy than leverage, forexample.

In some embodiments, the evaluation module evaluates a predictiveaccuracy by evaluating a goodness of fit of a calibration set with acalibration line drawn from the calibration set. In some embodiments, agoodness of fit is measured to determine how well the data that form aregression line actually fit with the regression line, which can becalculated as an average error (distribution) from the line, and/or aconfidence interval associated with the line drawn from a set of data,for example.

In some embodiments, the predictive accuracy is calculated in terms of apercentage change in baseline value in a dual electrode sensor, forexample, using the percent deviation formula defined as[(BV₁−BV₀)/BV₀]*100%,

where, BV₁ represents the baseline value at the end of the period andBV₀ represents the value of the baseline at the beginning of the period.For example, a certain percentage (e.g. 80%, 90% or 100%) of deviations(e.g., of the baseline value) are within a predefined deviation range(e.g., no more than 10, 15, 20, 25, 30, 35, 40, 50 or 60 mg/dL) and/orare not more than a predefined percent difference (e.g., 5%, 10%, 15%,20%, 25%, 30%, 35%, or 40%). In some embodiments, predictive accuracy iscalculated in terms of a certain deviation value within certainboundaries (e.g., clinical error grids and/or statistical thresholds).

Preferably, the evaluation module evaluates a predictive accuracy of thecalibration information using known statistical and/or clinical accuracymeasures. In some embodiments, the predictive accuracy is calculated interms of a percentage, for example a certain percentage of points withinpredefined bounds, for example, 60%, 70%, 80%, 85%, 90%, 95%, 98% or100% of data points with a predefined boundary, such as the A and Bzones of a Clark Error Grid. In some embodiments, the predictiveaccuracy is calculated in terms of a difference between the sensor dataand its corresponding reference data, for example, a certain percentage(e.g., 80%, 90% or 100%) of matched data pairs (e.g., in the calibrationset) are within a predefined glucose concentration range (e.g., no morethan 10, 15, 20, 25, 30, 35, 40, 50 or 60 mg/dL difference) and/or arenot more than a predefined percent difference (e.g., 5%, 10%, 15%, 20%,25%, 30%, 35%, or 40% percent difference) In some embodiments, apredictive accuracy is calculated in terms of an R or R-squared value,for example, when evaluating a correlation or goodness of fit. In someembodiments, predictive accuracy is calculated in terms of a certainnumber of points (or the last point) within certain boundaries (clinicalerror grids and/or statistical thresholds). In some embodiments, thepredictive accuracy is calculated on converted sensor data (e.g.,calibrated data such as glucose concentration in mg/dL). Alternatively,predictive accuracy is calculated on non-converted/non-calibrated sensordata.

At block 740, a determination module, also referred to as the processormodule, determines when to request additional reference data. Ingeneral, the processor module can be programmed to intermittentlyrequest additional reference data at predetermined times during a sensorsession and/or at times determined by the processor module during thesensor session to increase accuracy of sensor calibration with a minimumnumber of reference data requests (e.g., no more than about 2 per day,no more than about 1 per day, no more than about 7 per 7-day sensorsession, no more than about 5 per 7-day sensor session, no more thanabout 3 per 7-day sensor session, no more than about 3 per 3-day sensorsession and no more than about 2 per-3 day sensor session). In someembodiments, the processor module is configured to request additionalreference data after a time period determined in response to the resultsof the evaluation described above; this time period can be any timeperiod within the sensor session, for example, in a sensor configuredfor 7-days of in vivo use, the time period is between about 0 minutesand about 7 days. In one exemplary embodiment, after the evaluationprocess described with reference to block 730, the processor module 406is programmed to request additional reference data after a time perioddetermined by the determination module of from about 0-, 5-, 10-, 20-,30-, 60-minutes, or 2-, 4-, 6-, 9-, 12-, 18-, or 24-hours to about 1½-,2-, 3-, 4-, 5-, 6- or 7 days.

Accordingly, in some embodiments, the computer system (e.g., theprocessor module) is configured to request reference data at a timedetermined by the evaluation of the matched data pair and/or thecalibration set. In one exemplary embodiment, the computer system isconfigured to display an amount of time before a next reference datawill be requested.

In some embodiments, for example, when the evaluation module evaluates acorrelation of the new matched data pair with the calibration set, thedetermination module determines when to request additional referencedata based at least in part on the correlation of the new matched datapair and the calibration set.

In some embodiments, for example, when the evaluation module 730evaluates a correlation of the matched data pairs in an (existing)calibration set (e.g., a calibration set without a newly receivedmatched data pair) with a new calibration line (e.g., formed from a newcalibration set including the newly received matched data pair), thedetermination module 740 determines when to request additional referencedata based at least in part on the correlation of the matched pairs inthe calibration set and the new calibration line.

In some embodiments, for example, when the evaluation module 730evaluates a discordance of the new matched data pair and/or the matcheddata pairs in the new calibration set, the determination moduledetermines when to request additional reference data based at least inpart on the discordance of the new matched data pair and/or the matcheddata pairs in the new calibration set.

In some embodiments, for example, wherein the evaluation module 730iteratively evaluates a plurality of combinations of matched data pairsin the calibration set to obtain a plurality of calibration lines, thedetermination module 740 determines when to request additional referencedata based at least in part on the iterative evaluation.

In some embodiments, for example, wherein the evaluation module 730evaluates an accuracy of the calibration set, the determination module740 determines when to request additional reference data based at leastin part on the accuracy of the calibration line and an estimated glucoseconcentration.

Accordingly, the predictive accuracy of the calibration information suchas described above, can be quantified by the computer system (e.g.,processor module) and a time to next reference value determined (e.g.,in order to determine when the next reference value should berequested). In general, the results of the predictive accuracy are inputinto a model and a time to next reference data determined, wherein agreater predictive accuracy results in a longer time to next referencedata request and a lesser predictive accuracy results in a shorter timeto next reference data, which allows the number of reference datarequests to be minimized while ensuring a level of predictive accuracyin the sensor calibration. In some embodiments, the model includes alook-up table, wherein the results of the predictive accuracy arecompared against a table, and a time to next reference data requestdetermined. In some embodiments, the results of the predictive accuracyare input into a formula, function or equation, and the time to nextreference data request determined.

In some embodiments, the predictive accuracy is calculated and comparedagainst one or more thresholds (e.g., 1, 2, 3, 4, 5, 6, 7 or morethresholds or criteria), from which an output is determined. In oneexemplary embodiment, if the predictive accuracy is determined to bewithin a first range of accuracy (e.g., at least about 70%, 75%, 80%,85%, 90%, 95%), then additional reference data is/are not requested bythe processor module for (or is requested by the processor module after)a first request time period (e.g., 12, 24, 36, or 48 hours), wherein thefirst request time period is longer than other request time periods(described below). If the predictive accuracy is within a second rangeof accuracy (e.g., at least about 30%, 35%, 40%, 45%, 50%, 55%, 60%, or65%, and/or no more than about 65%, 70%, 75%, 80%, 85%, or 90%), thenadditional reference data is/are not requested by the processor modulefor (or is requested by the processor module after) a second requesttime period (e.g., 3 hours, 6 hours, 9 hours, 12 hours, or 18 hours),wherein the second request time period is less than the first timeperiod. If the predictive accuracy is within a third range of accuracy(e.g., no more than about 30%, 35%, 40%, 45%, 50%, 55%, 60%, or 65%),then additional reference data is/are not requested by the processormodule for (or is requested by the processor module after) a thirdrequest time period (e.g., 0 minutes, 30 minutes, 1 hour, 2 hours, 3hours, 6 hours, or 9 hours), wherein the third request time period isless than the second time period. Although one exemplary embodiment withthree ranges of accuracy is described above, one, two, three, four, fiveor more ranges, thresholds, criteria, and the like, can be used todetermine when to request additional reference data.

In some embodiments, wherein the evaluation module evaluates the glucoseconcentration(s) associated with the matched data pair(s) in thecalibration set, the processor module is configured to request referencedata based on present glucose concentration and glucose concentrationassociated with matched pairs in the cal set, for example, thedetermination module 740 is configured to request additional referencedata when the host's glucose concentration is at a level that wouldincrease the spread of the glucose concentrations associated with thematched data pairs in the calibration set. Advantageously, an increasedspread of matched data pairs in a calibration set increases accuracy ofthe calibration line.

In some embodiments, one or more of the above embodiments are combined;for example, the determination module 740 can be configured to determinewhen to request additional reference data based on accuracy of thematched data pairs in the calibration set and a spread of glucoseconcentrations associated with matched data pairs in the calibrationset.

In some embodiments, the dual electrode sensor (as described elsewhereherein) can be configured to provide information that can be used toevaluate a predictive accuracy. In an exemplary embodiment, thedual-electrode analyte sensor includes a non-enzyme or second workingelectrode that is configured to generate a signal associated withbackground noise. Certain fluctuations in the non-enzyme related signalcan be indicators of drift and can be quantified to give a predictiveaccuracy, which can provide an indication of when calibration is needed,for example.

In an exemplary embodiment, a regression analysis is performed tocalibrate information using the slope-intercept equation (y=mx+b), whichdefines a conversion function (described elsewhere herein), where thevalue of the baseline (b) is represented by the signal associated withthe background noise. A predictive accuracy can be calculated using thisequation by analyzing a deviation in the value of b over a period oftime (e.g. 1 min, 5 min, 10 min, 1 hour, 12 hours, 24 hours, 36 hours,48 hours, or more).

Additionally or alternatively to the determination module, thepredictive accuracy determined by the evaluation module enables decisionmaking of display, calibration, alarming, sensor health/diagnostics,insulin delivery, and the like. In some embodiments, the output module,or processor module, is configured to control an output based at leastin part on the predictive accuracy. In some embodiments, the system isconfigured to control a display (e.g., a user interface 416) based atleast in part on a predictive accuracy. In some embodiments, the systemis configured to control the display of raw and/or filtered data (e.g.,on a user interface or display) based at least in part on a predictiveaccuracy. In some embodiments, the system is configured to display rateof change information based at least in part on a predictive accuracy.In some embodiments, the system is configured to control alarmsindicative of at least one of hypoglycemia, hyperglycemia, predictedhypoglycemia, and predicted hyperglycemia based at least in part on apredictive accuracy. In some embodiments, the system is configured tocontrolling insulin delivery and/or insulin therapy instructions basedat least in part on a predictive accuracy, for example, when to fallback to a more conservative recommendation or when to open the loop(request user interaction) of a closed loop insulin delivery system. Insome embodiments, the system is configured to diagnose a sensorcondition based at least in part on a predictive accuracy. In someembodiments, the system is configured to suspend display of sensor databased at least in part on a predictive accuracy. In some embodiments,the system is configured to shut down a sensor session based at least inpart on a predictive accuracy.

Additional methods for processing sensor glucose data are disclosed inU.S. Patent Publication No. US-2005-0027463-A1. In view of theabove-described data processing, it is believed that improving theaccuracy of the data stream will be advantageous for improving output ofglucose sensor data. Accordingly, the following description is relatedto improving data output by decreasing signal artifacts on the raw datastream from the sensor. The data smoothing methods of preferredembodiments can be employed in conjunction with any sensor or monitormeasuring levels of an analyte in vivo, wherein the level of the analytefluctuates over time, including but not limited to such sensors asdescribed in U.S. Pat. No. 6,001,067 to Shults et al.; U.S. PatentPublication No. US-2003-0023317-A1 U.S. Pat. No. 6,212,416 to Ward etal.; U.S. Pat. No. 6,119,028 to Schulman et al; U.S. Pat. No. 6,400,974to Lesho; U.S. Pat. No. 6,595,919 to Berner et al.; U.S. Pat. No.6,141,573 to Kurnik et al.; U.S. Pat. No. 6,122,536 to Sun et al.;European Patent Application EP 1153571 to Varall et al.; U.S. Pat. No.6,512,939 to Colvin et al.; U.S. Pat. No. 5,605,152 to Slate et al.;U.S. Pat. No. 4,431,004 to Bessman et al.; U.S. Pat. No. 4,703,756 toGough et al; U.S. Pat. No. 6,514,718 to Heller et al; and U.S. Pat. No.5,985,129 to Gough et al.

Signal

Generally, implantable sensors measure a signal related to an analyte ofinterest in a host. For example, an electrochemical sensor can measureglucose, creatinine, or urea in a host, such as an animal (e.g., ahuman). Generally, the signal is converted mathematically to a numericvalue indicative of analyte status, such as analyte concentration, suchas described in more detail, above. The signal detected by the sensorcan be broken down into its component parts. For example, in anenzymatic electrochemical analyte sensor, preferably after sensorbreak-in is complete, the total signal can be divided into an “analytecomponent,” which is representative of analyte (e.g., glucose)concentration, and a “noise component,” which is caused bynon-analyte-related species that have a redox potential thatsubstantially overlaps with the redox potential of the analyte (ormeasured species, e.g., H₂O₂) at an applied voltage. The noise componentcan be further divided into its component parts, i.e., constant andnon-constant noise. It is not unusual for a sensor to experience acertain level of noise. In general, “constant noise” (sometimes referredto as constant background or baseline) is caused by non-analyte-relatedfactors that are relatively stable over time, including but not limitedto electroactive species that arise from generally constant (e.g.,daily) metabolic processes. Constant noise can vary widely betweenhosts. In contrast, “non-constant noise” (sometimes referred to asnon-constant background, signal artifacts, signal artifact events (orepisodes), transient noise, noise events, noise episodes, and the like)is caused by non-constant, non-analyte-related species (e.g.,non-constant noise-causing electroactive species) that arise duringtransient events, such as during host metabolic processes (e.g., woundhealing or in response to an illness), or due to ingestion of certaincompounds (e.g., certain drugs). In some circumstances, noise can becaused by a variety of noise-causing electroactive species, which arediscussed in detail elsewhere herein.

FIG. 8A is a graph illustrating the components of a signal measured by atranscutaneous glucose sensor (after sensor break-in was complete), in anon-diabetic volunteer host. The Y-axis indicates the signal amplitude(in counts) detected by the sensor. The term “counts” as used herein isa broad term, and is to be given its ordinary and customary meaning to aperson of ordinary skill in the art (and it is not to be limited to aspecial or customized meaning), and refers without limitation to a unitof measurement of a digital signal. In one example, a raw data streammeasured in counts is directly related to a voltage (for example,converted by an A/D converter), which is directly related to currentfrom a working electrode. The X-axis indicates time.

The total signal collected by the sensor is represented by line 800,which includes components related to glucose, constant noise, andnon-constant noise, which are described in more detail elsewhere herein.In some embodiments, the total signal is a raw data stream, which caninclude a signal averaged or integrated by a charge-counting device, forexample.

The non-constant noise component of the total signal is represented byline 802. The non-constant noise component 802 of the total signal 800can be obtained by filtering the total signal 800 to obtain a filteredsignal 804 using any of a variety of known filtering techniques, andthen subtracting the filtered signal 804 from the total signal 800. Insome embodiments, the total signal can be filtered using linearregression analysis of the n (e.g. 10) most recent sampled sensorvalues. In some embodiments, the total signal can be filtered usingnon-linear regression. In some embodiments, the total signal can befiltered using a trimmed regression, which is a linear regression of atrimmed mean (e.g. after rejecting wide excursions of any point from theregression line). In this embodiment, after the sensor records glucosemeasurements at a predetermined sampling rate (e.g. every 30 seconds),the sensor calculates a trimmed mean (e.g. removes highest and lowestmeasurements from a data set) and then regresses the remainingmeasurements to estimate the glucose value. In some embodiments, thetotal signal can be filtered using a non-recursive filter, such as afinite impulse response (FIR) filter. An FIR filter is a digital signalfilter, in which every sample of output is the weighted sum of past andcurrent samples of input, using only some finite number of past samples.In some embodiments, the total signal can be filtered using a recursivefilter, such as an infinite impulse response (IIR) filter. An IIR filteris a type of digital signal filter, in which every sample of output isthe weighted sum of past and current samples of input. In someembodiments, the total signal can be filtered using a maximum-average(max-average) filtering algorithm, which smoothes data based on thediscovery that the substantial majority of signal artifacts observedafter implantation of glucose sensors in humans, for example, is notdistributed evenly above and below the actual blood glucose levels. Ithas been observed that many data sets are actually characterized byextended periods in which the noise appears to trend downwardly frommaximum values with occasional high spikes. To overcome these downwardtrending signal artifacts, the max-average calculation tracks with thehighest sensor values, and discards the bulk of the lower values.Additionally, the max-average method is designed to reduce thecontamination of the data with unphysiologically high data from the highspikes. The max-average calculation smoothes data at a sampling interval(e.g. every 30 seconds) for transmission to the receiver at a lessfrequent transmission interval (e.g. every 5 minutes), to minimize theeffects of low non-physiological data. First, the processor finds andstores a maximum sensor counts value in a first set of sampled datapoints (e.g. 5 consecutive, accepted, thirty-second data points). Aframe shift time window finds a maximum sensor counts value for each setof sampled data (e.g. each 5-point cycle length) and stores each maximumvalue. The processor then computes a rolling average (e.g. 5-pointaverage) of these maxima for each sampling interval (e.g. every 30seconds) and stores these data. Periodically (e.g. every 10^(th)interval), the sensor outputs to the receiver the current maximum of therolling average (e.g. over the last 10 thirty-second intervals as asmoothed value for that time period (e.g. 5 minutes)). In someembodiments, the total signal can be filtered using a “Cone ofPossibility Replacement Method,” which utilizes physiologicalinformation along with glucose signal values in order define a “cone” ofphysiologically feasible glucose signal values within a human.Particularly, physiological information depends upon the physiologicalparameters obtained from continuous studies in the literature as well asour own observations. A first physiological parameter uses a maximalsustained rate of change of glucose in humans (e.g. about 4 to 5mg/dl/min) and a maximum sustained acceleration of that rate of change(e.g. about 0.1 to 0.2 mg/min/min). A second physiological parameteruses the knowledge that rate of change of glucose is lowest at themaxima and minima, which are the areas of greatest risk in patienttreatment. A third physiological parameter uses the fact that the bestsolution for the shape of the curve at any point along the curve over acertain time period (e.g. about 20-25 minutes) is a straight line. It isnoted that the maximum rate of change can be narrowed in some instances.Therefore, additional physiological data can be used to modify thelimits imposed upon the Cone of Possibility Replacement Method forsensor glucose values. For example, the maximum per minute rate changecan be lower when the subject is lying down or sleeping; on the otherhand, the maximum per minute rate change can be higher when the subjectis exercising, for example. In some embodiments, the total signal can befiltered using reference changes in electrode potential to estimateglucose sensor data during positive detection of signal artifacts froman electrochemical glucose sensor, the method hereinafter referred to asreference drift replacement. In this embodiment, the electrochemicalglucose sensor comprises working, counter, and reference electrodes.This method exploits the function of the reference electrode as itdrifts to compensate for counter electrode limitations during oxygendeficits, pH changes, and/or temperature changes. In alternativeimplementations of the reference drift method, a variety of algorithmscan therefore be implemented based on the changes measured in thereference electrode. Linear algorithms, and the like, are suitable forinterpreting the direct relationship between reference electrode driftand the non-glucose rate limiting signal noise such that appropriateconversion to signal noise compensation can be derived. Additionaldescription of signal filtering can be found in more detail elsewhereherein.

Referring again to FIG. 8A, the constant noise signal component 806 canbe obtained by calibrating the sensor signal using reference data, suchas one or more blood glucose values obtained from a hand-held bloodglucose meter, from which the baseline “b” of a regression can beobtained, representing the constant noise signal component 806.

The analyte signal component 808 can be obtained by subtracting theconstant noise signal component 806 from the filtered signal 804.

Noise

Noise is clinically important because it can induce error and can reducesensor performance, such as by providing a signal that causes theanalyte concentration to appear higher or lower than the actual analyteconcentration. For example, upward or high noise (e.g. noise that causesthe signal to increase) can cause the host's glucose concentration toappear higher than it truly is which can lead to improper treatmentdecisions. Similarly, downward or low noise (e.g., noise that causes thesignal to decrease) can cause the host's glucose concentration to appearlower than it is which can also lead to improper treatment decisions.

Noise can be caused by a variety of factors, ranging from mechanicalfactors to biological factors. For example, it is known that macro- ormicro-motion, ischemia, pH changes, temperature changes, pressure,stress, or even unknown mechanical, electrical, and/or biochemicalsources can cause noise, in some embodiments. Interfering species, whichare known to cause non-constant noise, can be compounds, such as drugsthat have been administered to the host, or intermittently producedproducts of various host metabolic processes. Exemplary interferentsinclude but are not limited to a variety of drugs (e.g. acetaminophen),H₂O₂ from exterior sources (e.g. produced outside the sensor membranesystem), and reactive metabolic species (e.g. reactive oxygen andnitrogen species, some hormones, etc.). Some known interfering speciesfor a glucose sensor include but are not limited to acetaminophen,ascorbic acid, bilirubin, cholesterol, creatinine, dopamine, ephedrine,ibuprofen, L-dopa, methyldopa, salicylate, tetracycline, tolazamide,tolbutamide, triglycerides, and uric acid.

In some experiments of implantable glucose sensors, it was observed thatnoise increased when some hosts were intermittently sedentary, such asduring sleep or sitting for extended periods. When the host began movingagain, the noise quickly dissipated. Noise that occurs duringintermittent, sedentary periods (sometimes referred to as intermittentsedentary noise) can occur during relatively inactive periods, such assleeping. Non-constant, non-analyte-related factors can causeintermittent sedentary noise, such as was observed in one exemplarystudy of non-diabetic individuals implanted with enzymatic-type glucosesensors built without enzyme. These sensors (without enzyme) could notreact with or measure glucose and therefore provided a signal due tonon-glucose effects only (e.g., constant and non-constant noise). Duringsedentary periods (e.g., during sleep), extensive, sustained signal wasobserved on the sensors. Then, when the host got up and moved around,the signal rapidly corrected. As a control, in vitro experiments wereconducted to determine if a sensor component might have leached into thearea surrounding the sensor and caused the noise, but none was detected.From these results, it is believed that a host-produced non-analyterelated reactant was diffusing to the electrodes and producing theunexpected non-constant noise signal.

While not wishing to be bound by theory, it is believed that aconcentration increase of noise-causing electroactive species, such aselectroactive metabolites from cellular metabolism and wound healing,can interfere with sensor function and cause noise observed during hostsedentary periods. For example, local lymph pooling, which can occurwhen a part of the body is compressed or when the body is inactive, cancause, in part, this local build up of interferants (e.g. electroactivemetabolites). Similarly, a local accumulation of wound healing metabolicproducts (e.g. at the site of sensor insertion) likely causes noise onthe sensor. Noise-causing electroactive species can include but are notlimited to compounds with electroactive acidic, amine or sulfhydrylgroups, urea, lactic acid, phosphates, citrates, peroxides, amino acids(e.g. L-arginine), amino acid precursors or break-down products, nitricoxide (NO), NO-donors, NO-precursors or other electroactive species ormetabolites produced during cell metabolism and/or wound healing, forexample. For a more complete discussion of noise and its sources, seeU.S. Patent Publication No. US-2007-0027370-A1.

Noise can be recognized and/or analyzed in a variety of ways. Forexample, in some circumstances, non-constant noise changes faster thanthe analyte signal and/or does not follow an expected analyte signalpattern; and lasts for a period of about 10 hours or more, 8 hours, 6hours, 4 hours, 2 hours, 60 minutes, 30 minutes, or 10 minutes or less.In some embodiments, the sensor data stream can be monitored, signalartifacts detected, and data processing performed based at least in parton whether or not a signal artifact has been detected, such as describedin more detail elsewhere herein.

In some conventional analyte sensors, non-constant noise can be asignificant component of the total signal, for example, 30%, 40%, 50%,60% or more of the total signal. Additionally, non-constant noise canoccur for durations of minutes or hours, in some circumstances. In somecircumstances, non-constant noise can be equivalent to a glucoseconcentration of about 400-mg/dl or more. Noise can induce error in thesensor reading, which can reduce sensor accuracy and clinically usefuldata. However, a high level of sensor accuracy is critical forsuccessful patient care and desirable clinical outcomes.

In some embodiments, an electrochemical analyte detection system isprovided, which includes a sensor configured for substantiallycontinuous analyte detection, such as in an ambulatory host. The sensorincludes at least one electrode and electronics configured to provide asignal measured at the electrode; wherein the measured signal can bebroken down (e.g. after sensor break-in) into its component parts, asubstantially analyte-related component, a substantially constantnon-analyte-related component (i.e., constant noise) and a substantiallynon-constant non-analyte-related component (i.e., non-constant noise).

In some embodiments, a signal component's percentage of the total signalis determined using one or more of a variety of methods of quantifyingan amplitude of signal components and total signal, from which eachcomponents percent contribution can be calculated, as is appreciated byone skilled in the art. In some embodiments, the signal component(s) canbe quantified by comparing the peak-to-peak amplitudes of each signalcomponent for a time period, whereby the peak-to-peak amplitudes of eachcomponent can be compared to the peak-to-peak amplitude of the totalsignal to determine its percentage of the total signal, as isappreciated by one skilled in the art. In some embodiments, the signalcomponent(s) can be quantified by determining the Root Mean Square (RMS)of the signal component for a time period. In one exemplary of Root MeanSquare analysis of signal components, the signal component(s) can bequantified using the formula:

${RMS} = \sqrt{\frac{\sum\left( {x_{1}^{2} + x_{2}^{2} + x_{3}^{2} + x_{n}^{2}} \right)}{n}}$wherein there are a number (n) of data values (x) for a signal (e.g.analyte component, non-constant noise component, constant noisecomponent, and/or total signal) during a predetermined time period (e.g.about 1 day, about 2 days, about 3 days, etc). Once the signalcomponents and/or total signal are quantified, the signal components canbe compared to the total signal to determine a percentage of each signalcomponent within the total signal.Signal Artifact Detection and Replacement

Typically, a glucose sensor produces a data stream that is indicative ofthe glucose concentration of a host, such as described in more detailabove. However, it is well known that of the glucose sensors describedabove, there are only a few examples of glucose sensors that are able toprovide a raw data output indicative of the concentration of glucose.Thus, it should be understood that the systems and methods describedherein, including signal artifacts detection, signal artifactsreplacement, and other data processing, can be applied to a data streamobtained from any glucose sensor.

Raw data streams typically have some amount of “system noise,” caused byunwanted electronic or diffusion-related noise that degrades the qualityof the signal and thus the data. Accordingly, conventional glucosesensors are known to smooth raw data using methods that filter out thissystem noise, and the like, in order to improve the signal to noiseratio, and thus data output. One example of a conventionaldata-smoothing algorithm includes a finite impulse response filter(FIR), which is particularly suited for reducing high-frequency noise(see Steil et al. U.S. Pat. No. 6,558,351).

FIGS. 8B and 8C are graphs of raw data streams from an implantableglucose sensor prior to data smoothing in one embodiment. FIG. 8B is agraph that shows a raw data stream obtained from a glucose sensor overan approximately 4 hour time span in one example. FIG. 8C is a graphthat shows a raw data stream obtained from a glucose sensor over anapproximately 36 hour time span in another example. The x-axisrepresents time in minutes. The y-axis represents sensor data in counts.In these examples, sensor output in counts is transmitted every30-seconds.

The “system noise” such as shown in sections 810 a, 810 b of the datastreams of FIGS. 8B and 8C, respectively, illustrate time periods duringwhich system noise can be seen on the data stream. This system noise canbe characterized as Gaussian, Brownian, and/or linear noise, and can besubstantially normally distributed about the mean. The system noise islikely electronic and diffusion-related, and the like, and can besmoothed using techniques such as by using an FIR filter. As anotherexample, the raw data can be represented by an integrated value, forexample, by integrating the signal over a time period (e.g., 30 secondsor 5 minutes), and providing an averaged (e.g., integrated) data pointthere from. The system noise such as shown in the data of sections 810a, 810 b is a fairly accurate representation of glucose concentrationand can be confidently used to report glucose concentration to the userwhen appropriately calibrated.

The “signal artifacts,” also referred to as “signal artifact events” or“noise episodes” for example, such as shown in sections 812 a, 812 b ofthe data stream of FIGS. 8B and 8C, respectively, illustrate timeperiods during which “signal artifacts” can be seen, which aresignificantly different from the previously described system noise(sections 810 a, 810 b). This noise, such as shown in section 812 a and812 b, is referred to herein as “signal artifacts” and may be describedas “transient non-glucose dependent signal artifacts that have higheramplitude than system noise.” At times, signal artifacts comprise lownoise, which generally refers to noise that substantially decreasessignal amplitude 814 a, 814 b herein, which is best seen in the signalartifacts 812 b of FIG. 8C. Occasional high spikes 816 a, 816 b, whichgenerally correspond to noise that substantially increases signalamplitude, can also be seen in the signal artifacts, which generallyoccur after a period of low noise. These high spikes are generallyobserved after transient low noise and typically result after reactionrate-limiting phenomena occur. For example, in an embodiment where aglucose sensor requires an enzymatic reaction, local ischemia creates areaction that is rate-limited by oxygen, which is responsible for lownoise. In this situation, glucose would be expected to build up in themembrane because it would not be completely catabolized during theoxygen deficit. When oxygen is again in excess, there would also beexcess glucose due to the transient oxygen deficit. The enzyme ratewould speed up for a short period until the excess glucose iscatabolized, resulting in high noise. Additionally, noise can bedistributed both above and below the expected signal.

Analysis of signal artifacts such as shown sections 812 a, 812 b ofFIGS. 8B and 8C, respectively, indicates that the observed low noise iscaused by substantially non-glucose reaction dependent phenomena, suchas ischemia that occurs within or around a glucose sensor in vivo, forexample, which results in the reaction becoming oxygen dependent. As afirst example, at high glucose levels, oxygen can become limiting to theenzymatic reaction, resulting in a non-glucose dependent downward trendin the data (best seen in FIG. 8C). As a second example, certainmovements or postures taken by the patient can cause transient downwardnoise as blood is squeezed out of the capillaries resulting in localischemia, and causing non-glucose dependent low noise. Because excessoxygen (relative to glucose) is necessary for proper sensor function,transient ischemia can result in a loss of signal gain in the sensordata. In this second example oxygen can also become transiently limiteddue to contracture of tissues around the sensor interface. This issimilar to the blanching of skin that can be observed when one putspressure on it. Under such pressure, transient ischemia can occur inboth the epidermis and subcutaneous tissue. Transient ischemia is commonand well tolerated by subcutaneous tissue.

In another example of non-glucose reaction rate-limiting phenomena, skintemperature can vary dramatically, which can result in thermally relatederosion of the signal (e.g., temperature changes between 32 and 39degrees Celsius have been measured in humans). In yet anotherembodiment, wherein the glucose sensor is placed intravenously,increased impedance can result from the sensor resting against wall ofthe blood vessel, for example, producing this non-glucose reactionrate-limiting noise due to oxygen deficiency.

FIG. 9 is a flow chart 900 that illustrates the process of detecting andreplacing signal artifacts in certain embodiments. It is noted that“signal artifacts” particularly refers to the transient non-glucoserelated artifacts such as described in more detail elsewhere herein.Typically, signal artifacts are caused by non-glucose rate-limitingphenomenon such as described in more detail above.

At block 910, a sensor data receiving module, also referred to as thesensor data module 910, or processor module, receives sensor data (e.g.,a data stream), including one or more time-spaced sensor data points. Insome embodiments, the data stream is stored in the sensor for additionalprocessing; in some alternative embodiments, the sensor periodicallytransmits the data stream to the receiver 300, which can be in wired orwireless communication with the sensor. In some embodiments, raw and/orfiltered data is stored in the sensor and/or receiver.

At block 912, a signal artifacts detection module, also referred to asthe signal artifacts detector 914 or signal reliability module, isprogrammed to detect transient non-glucose related signal artifacts inthe data stream. The signal artifacts detector can comprise an oxygendetector, a pH detector, a temperature detector, and/or apressure/stress detector, for example, the signal artifacts detector 228in FIG. 2. In some embodiments, the signal artifacts detector at block912 is located within the processor 214 in FIG. 2 and utilizes existingcomponents of the glucose sensor to detect signal artifacts, for exampleby pulsed amperometric detection, counter electrode monitoring,reference electrode monitoring, and frequency content monitoring, whichare described elsewhere herein. In yet other embodiments, the datastream can be sent from the sensor to the receiver which comprisesprogramming in the processor 406 in FIG. 4A that performs algorithms todetect signal artifacts, for example such as described with reference to“Cone of Possibility Detection” method and/or by comparing raw data vs.filtered data, both of which are described in more detail below.Numerous embodiments for detecting signal artifacts are described inmore detail in the section entitled, “Signal Artifacts Detection andReplacement,” all of which are encompassed by the signal artifactsdetection at block 912.

In certain embodiments, the processor module in either the sensorelectronics and/or the receiver electronics can evaluate an intermittentor continuous signal-to-noise measurement to determine aberrancy ofsensor data responsive to a signal-to-noise ratio above a set threshold.In certain embodiments, signal residuals (e.g., by comparing raw andfiltered data) can be intermittently or continuously analyzed for noiseabove a set threshold. In certain embodiments, pattern recognition canbe used to identify noise associated with physiological conditions, suchas low oxygen, or other known signal aberrancies. Accordingly, in theseembodiments, the system can be configured, in response to aberrancies inthe data stream, to trigger signal estimation, adaptively filter thedata stream according to the aberrancy, and the like, as described inmore detail elsewhere herein.

At block 914, the signal artifacts replacement module, also referred toas the signal estimation module, replaces some or an entire data streamwith estimated glucose signal values using signal estimation. Numerousembodiments for performing signal estimation are described in moredetail in the section entitled “Signal Artifacts Detection andReplacement,” all of which are encompassed by the signal artifactsreplacement module, block 914. It is noted that in some embodiments,signal estimation/replacement is initiated in response to positivedetection of signal artifacts on the data stream, and subsequentlystopped in response to detection of negligible signal artifacts on thedata stream. In some embodiments, the system waits a predetermined timeperiod (e.g., between 30 seconds and 30 minutes) before switching thesignal estimation on or off to ensure that a consistent detection hasbeen ascertained. In some embodiments, however, signalestimation/replacement can continuously or continually run.

Some embodiments of signal estimation can additionally includediscarding data that is considered sufficiently unreliable and/orerroneous such that the data should not be used in a signal estimationalgorithm. In these embodiments, the system can be programmed to discardoutlier data points, for example data points that are so extreme thatthey can skew the data even with the most comprehensive filtering orsignal estimation, and optionally replace those points with a projectedvalue based on historical data or present data (e.g., linear regression,recursive filtering, and the like). One example of discarding sensordata includes discarding sensor data that falls outside of a “Cone ofPossibility” such as described in more detail elsewhere herein. Anotherexample includes discarding sensor data when signal artifacts detectiondetects values outside of a predetermined threshold (e.g., oxygenconcentration below a set threshold, temperature above a certainthreshold, signal amplitude above a certain threshold, etc). Any of thesignal estimation/replacement algorithms described herein can then beused to project data values for those data that were discarded.

Analysis of signals from glucose sensors indicates at least two types ofnoise, which are characterized herein as 1) system noise and 2) signalartifacts, such as described in more detail above. It is noted thatsystem noise is easily smoothed using the algorithms provided herein;however, the systems and methods described herein particularly addresssignal artifacts, by replacing transient erroneous signal noise causedby rate-limiting phenomenon (e.g., non-glucose related signal) withestimated signal values, for example.

In certain embodiments of signal artifacts detection, oxygen monitoringis used to detect whether transient non-glucose dependent signalartifacts due to ischemia. Low oxygen concentrations in or near theglucose sensor can account for a large part of the transient non-glucoserelated signal artifacts as defined herein on a glucose sensor signal,particularly in subcutaneously implantable glucose sensors. Accordingly,detecting oxygen concentration, and determining if ischemia exists candiscover ischemia-related signal artifacts. A variety of methods can beused to test for oxygen. For example, an oxygen-sensing electrode, orother oxygen sensor can be employed. The measurement of oxygenconcentration can be sent to a processor, which determines if the oxygenconcentration indicates ischemia.

Additional description of signal artifact detection and replacement canbe found in U.S. Patent Publication Nos. 2005/0043598, 2007/0032706,2007/0016381, and 2007/0066873, and U.S. patent application Ser. No.11/762,638, filed on Jun. 13, 2007 and entitled “SYSTEMS AND METHODS FORREPLACING SIGNAL ARTIFACTS IN A GLUCOSE SENSOR DATA STREAM,” all ofwhich are incorporated by reference herein in their entirety.

In one embodiment of signal artifacts detection that utilizesexamination or evaluation of the signal information content, filtered(e.g., smoothed) data is compared to raw data (e.g., in sensorelectronics or in receiver electronics). In one such embodiment, asignal residual is calculated as the difference between the filtereddata and the raw data. For example, at one time point (or one timeperiod that is represented by a single raw value and single filteredvalue), the filtered data can be measured at 50,000 counts and the rawdata can be measured at 55,500 counts, which would result in a signalresidual of 5,500 counts. In some embodiments, a threshold can be set(e.g., 5000 counts) that represents a first level of noise (e.g., signalartifact) in the data signal, when the residual exceeds that level.Similarly, a second threshold can be set (e.g., 8,000 counts) thatrepresents a second level of noise in the data signal. Additionalthresholds and/or noise classifications can be defined as is appreciatedby one skilled in the art. Consequently, signal filtering, processing,and/or displaying decisions can be executed based on these conditions(e.g., the predetermined levels of noise).

Although the above-described example illustrates one method ofdetermining a level of noise, or signal artifact(s), based on acomparison of raw vs. filtered data for a time point (or single valuesrepresentative of a time period), a variety of alternative methods arecontemplated. In an alternative exemplary embodiment for determiningnoise, signal artifacts are evaluated for noise episodes lasting acertain period of time. For example, the processor (in the sensor orreceiver) can be configured to look for a certain number of signalresiduals above a predetermined threshold (representing noise timepoints or noisy time periods) for a predetermined period of time (e.g.,a few minutes to a few hours or more).

In one exemplary embodiment, a processor is configured to determine asignal residual by subtracting the filtered signal from the raw signalfor a predetermined time period. It is noted that the filtered signalcan be filtered by any known smoothing algorithm such as describedherein, for example a 3-point moving average-type filter. It is furthernoted that the raw signal can include an average value, e.g., whereinthe value is integrated over a predetermined time period (such as5-minutes). Furthermore, it is noted that the predetermined time periodcan be a time point or representative data for a time period (e.g., 5minutes). In some embodiments, wherein a noise episode for apredetermined time period is being evaluated, a differential (deltaresidual) can be obtained by comparing a signal residual with a previoussignal residual (e.g., a residual at time (t)=0 as compared to aresidual at (t)−5 minutes.) Similar to the thresholds described abovewith regard to the signal residual, one or more thresholds can be setfor the differentials, whereby one or more differentials above one ofthe predetermined differential thresholds defines a particular noiselevel. It has been shown in certain circumstances that a differentialmeasurement as compared to a residual measurement as described herein,amplifies noise and therefore may be more sensitive to noise episodes,without increasing false positives due to fast, but physiological, ratesof change. Accordingly, a noise episode, or noise episode level, can bedefined by one or more points (e.g., residuals or differentials) above apredetermined threshold, and in some embodiments, for a predeterminedperiod of time. Similarly, a noise level determination can be reduced oraltered when a different (e.g., reduced) number of points above thepredetermined threshold are calculated in a predetermined period oftime.

In some embodiments, the amplitude of total signal, which can also bedescribed as power of the total signal, analyte signal (with or withoutbaseline (e.g., non-constant noise)), and/or non-constant noise, isperiodically or continuously obtained using methods such as aredescribed in more detail elsewhere herein (e.g., RMS method), whereinthe amplitude is a measure of the strength of the signal component. Insome embodiments, signal artifact events are detected by analysis ofamplitudes of various signal components, such as the amplitude of thenon-constant noise component as compared to the amplitude of the analytesignal (with or without baseline).

In some embodiments, a start of a signal artifact event is determinedwhen the amplitude (power) of a signal artifact meets a firstpredetermined condition. In one embodiment, the first predeterminedcondition includes a residual amplitude of at least about 2, 3, 4, 5, 6,7, 8, 9, 10, 12, 14, 16, 18, 20 or 25% of the total signal or analytesignal amplitude (with or without baseline). In another embodiment, thefirst predetermined condition includes a differential amplitude(amplitude of a differential) of at least about 2, 3, 4, 5, 6, 7, 8, 9,10, 12, 14, 16, 18, 20 or 25% of the total signal or analyte signalamplitude (with or without baseline). In some embodiments, the firstpredetermined condition includes a plurality of points (e.g.,non-constant noise signal, residual, or differential) within apredetermined period (e.g., 5, 10, 30, or 60 minutes) above apredetermined threshold (e.g., an amplitude or a percentage amplitude),wherein the plurality of points includes 2, 3, 4, 5, 6, 7, 8 or morevalues.

In some embodiments, an end of a signal artifact event is determinedwhen then the amplitude (power) of a signal artifact meets a secondpredetermined condition. In one embodiment, the second predeterminedcondition includes a residual amplitude of no more than about 2, 3, 4,5, 6, 7, 8, 9, 10, 12, 14, 16, 18, 20 or 25% of the total signal oranalyte signal amplitude (with or without baseline). In anotherembodiment, the second predetermined condition comprises a differentialamplitude of no more than about 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 16,18, 20 or 25% of the total signal or analyte signal amplitude (with orwithout baseline). In some embodiments, the second predeterminedcondition includes a plurality of points (e.g., non-constant noisesignal, residual, or differential) within a predetermined period (e.g.,5, 10, 30, or 60 minutes) below a predetermined threshold (e.g., anamplitude or a percentage amplitude), wherein the plurality of pointsincludes 2, 3, 4, 5, 6, 7, 8 or more values.

Preferably, the system is configured to use hysteresis to processsignals so that the output (start/end (on/off) of noise episodes) reactsslowly by taking recent history into account; this prevents rapidswitching on and off as the glucose signal drifts around a threshold,for example. In some embodiments, the first predetermined condition isdifferent from the second predetermined condition. In some embodiments,the second condition includes criteria such as waiting a time period(e.g., 20, 30, 40, 60, 90 minutes, or more) after the start of a noiseepisode before determining an end of the noise episode. In someembodiments, the second condition includes criteria such as waitinguntil the signal increases beyond a predetermined threshold beforedetermining an end of the noise episode, wherein the predeterminedthreshold can be higher than another threshold within the secondcondition that triggers an end of the noise episode. In someembodiments, the first and second conditions have different amplitude(power) thresholds. In some embodiments, the first and second conditionshave different window lengths over which the evaluation is done. Whilenot wishing to be bound by theory, it is believed that condition(s) fordetermining a start of a noise episode can be different fromcondition(s) for determining an end of a noise episode. Accordingly, useof different start and end thresholds can reduce toggling betweenstart/end (or on/off) modes of the noise episode too quickly. In oneexemplary embodiment, the system is configured to determine a start of anoise episode when the non-constant noise is at least about 10% of theanalyte signal and the system is configured to determine an end of anoise episode at least about one hour after the start of the noiseepisode and when the non-constant noise is no more than about 10% of theanalyte signal.

One or a plurality of the above signal artifacts detection models can beused alone or in combination to detect signal artifacts such asdescribed herein. Accordingly, the data stream associated with thesignal artifacts can be discarded, replaced, or otherwise processed inorder to reduce or eliminate these signal artifacts and thereby improvethe value of the glucose measurements that can be provided to a user.Although much of the following description is drawn to replacing signalartifacts, circumstances exist wherein signal noise is too severe and/ortoo long in duration to replace. In some embodiments, the system isconfigured to determine whether a signal artifact and/or signal artifactepisode has exceeded a predetermined threshold. If the threshold isexceeded, then data is not displayed (e.g., rather than replacing thesignal as described in more detail, below). In some embodiments, asignal artifact and/or signal artifact episode threshold of (e.g.,absolute threshold and/or relative threshold such as high signalamplitude threshold, high noise amplitude threshold, and/or percentagethreshold) is used. In some embodiments, a signal artifact and/or signalartifact episode threshold of at least about 20, 30, 40, 60, 90, 120,180 minutes or more duration is used.

Reference is now made to FIG. 10, which is a flow chart 1000 thatillustrates the process of noise classification of the glucose sensorsignal in one embodiment. Preferably, the system is configured toclassify a level of noise in a signal obtained from a continuous analytesensor, for example, numerically and/or into groups. Advantageously,classification of the noise level enables decision making of display,calibration, alarming, sensor health/diagnostics, insulin delivery, andthe like.

At block 1010, a sensor data receiving module, also referred to as thesensor data module, or processor module, receives sensor data (e.g., adata stream), including one or more time-spaced sensor data pointshereinafter referred to as “data stream,” “sensor data,” “sensor analytedata”, “signal,” from a sensor via the receiver, which can be in wiredor wireless communication with the sensor. The sensor data receivingmodule is described in more detail elsewhere herein, for example, withreference to FIG. 5.

At block 1020, the processor module optionally filters the signalreceived from the continuous analyte sensor. Filtering can beaccomplished by sensor electronics and/or the receiver electronics,which are both encompassed by the term “computer system.”

At block 1030, a noise classification module, also referred to as theprocessor module, classifies a level of noise on the signal. In general,one or more noise classification thresholds and/or criteria areprogrammed within the computer system and/or adaptively determined bythe computer system.

In some embodiments, the signal noise is classified numerically and/orby grouping. In one exemplary embodiment, the level of noise isclassified as light, medium, and heavy. In another exemplary embodiment,the noise is classified as level1, level2, and level3, etc. However, anytypes or numbers of classifications associated with predeterminedthresholds and/or criteria can be used. For example, in someembodiments, the noise thresholds are predetermined (e.g., programmedinto the computer system (e.g., receiver)).

In some preferred embodiments, the noise thresholds are adaptivelydetermined by the computer system (e.g., for each sensor and/oriteratively during a sensor session) based on a signal strength of thesensor signal, which enables the noise thresholds to be customized tothe signal strength of the sensor and/or sensor in a particular host.

In one exemplary embodiment, the processor module applies a low passfilter to the signal (raw or filtered) to determine a signal strengththereof Although a first order low pass filter may be preferred in someembodiments, other orders (2^(nd), 3^(rd), etc) are also possible.

In some further embodiments, for example, wherein the processor moduleapplies a low pass filter to the signal to determine a signal strength,the system is configured to define one or more noise thresholds forclassification of the level of noise on the signal based at least inpart on a percentage of the signal strength (e.g., NoiseThreshold1corresponds to a first percentage of signal strength, NoiseThreshold2corresponds to a second percentage of signal strength, Noise Threshold3corresponds to a third percentage of signal strength).

Conventionally, filters are configured to filter the signal to providean average of the signal. However, in some preferred embodiments, thesystem is configured to “track the noise envelope,” (i.e., to track theouter amplitude of noise (e.g., without fluctuations) to obtain a worstcase scenario of noise on the sensor signal). In some embodiments, lowpass filters, RMS methods, and/or median filters applied to wide windowsof data (e.g., greater than or equal to about 30 min.) are applied tothe sensor signal. Advantageously, low pass filters track the slowvarying DC component (i.e., signal strength) of the analyte signal andcan be used to determine noise thresholds, which are described in moredetail, below.

In some embodiments, the processor module is configured to apply one ormore low pass filters to the noise signal to obtain one or more noiseindicators and subsequently compare the noise indicators with one ormore noise thresholds (either predefined noise thresholds or adaptivelydetermined noise thresholds). In the exemplary embodiment wherein thesystem is configured to use a first order low pass filter to definenoise thresholds for noise classification, the system is furtherconfigured to vary a coefficient of a low pass filter (e.g., the same oranother low pass filter), for example, by using the noise thresholdsdefined as percentage of the signal strength, to detect noise of varyingthresholds. Namely, the processor module is configured to apply one ormore low pass filters to the noise signal to obtain one or more noiseindicators and compare the noise indicators with one or more noisethresholds.

Additionally or alternatively, other methods of filtering, such asdetermining a median point within a window of data, detecting a maximumpoint within a window of data, and/or the like can be used to determinedthe signal strength and/or classify noise based on thresholds.

In some alternative embodiments, a root mean square (RMS) method can beused to determine signal strength.

In some alternative embodiments, spectral analysis can be used todetermine a signal strength and classify noise.

In some embodiments, the noise signal is a signal residual obtained bycomparing a raw signal to a filtered signal; however the differential ofthe residual (delta residual), absolute delta residual, and/or the likecan also be used as the noise signal input to one or more of thefilters.

In some embodiments, the processor module is configured to usehysteresis in classifying a level of noise in order to avoid waverbetween noise classifications. For example, by defining differentcriteria for returning back to a previous noise classification (e.g., agreater number of data points (or time) in a noise level to return to aprevious level than to move to the level initially).

At block 1040, the output module, or processor module, is configured tocontrol an output based at least in part on the noise classificationdetermined at block 1030. In general, the system is configured tocontrol the output based on a level of resolution of the signal, a levelof confidence in the signal, and/or a level of reliability of thesignal.

In some embodiments, the system is configured to control a display(e.g., a user interface 416) based at least in part on a noiseclassification. In some embodiments, the system is configured to controlthe display of raw and/or filtered data (e.g., on a user interface ordisplay) based at least in part on a noise classification. In someembodiments, the system is configured to display rate of changeinformation based at least in part on a noise classification. In someembodiments, the system is configured to control alarms indicative of atleast one of hypoglycemia, hyperglycemia, predicted hypoglycemia, andpredicted hyperglycemia based at least in part on a noiseclassification. In some embodiments, the system is configured tocontrolling medicament delivery (e.g., insulin delivery) and/or therapyinstructions based at least in part on a noise classification, forexample, when to fall back to a more conservative recommendation or whento open the loop (request user interaction) of a closed loop deliverysystem. In some embodiments, the system is configured to diagnose asensor condition (e.g., sensor failure) based at least in part on anoise classification. In some embodiments, the system is configured tosuspend display of sensor data based at least in part on a noiseclassification. In some embodiments, the system is configured to shutdown a sensor session based at least in part on a noise classification.

In some embodiments, the system is configured to display the noiseclassification on the user interface 416. In some embodiments, thesystem is configured to display information indicative of a level ofnoise on the sensor signal (e.g., light/medium/heavy orlevel1/level2/level3). In some embodiments, the system is configured todisplay information indicative of an amount of time that the signal hasbeen classified as having a level of noise (e.g., a time-elapsedcounter).

Signal Artifacts Replacement

One or a plurality of the above signal artifacts detection models can beused alone or in combination to detect signal artifacts (e.g., alevel/classification of noise on the signal) such as described herein.Accordingly, the data stream associated with the signal artifacts can bediscarded, replaced, or otherwise processed in order to reduce oreliminate these signal artifacts and thereby improve the value of theglucose measurements that can be provided to a user.

In some embodiments, Signal Artifacts Replacement can use systems andmethods that reduce or replace these signal artifacts that can becharacterized by transience, high frequency, high amplitude, and/orsubstantially non-linear noise. Accordingly, a variety of filters,algorithms, and other data processing are provided that address thedetected signal artifacts by replacing the data stream, or a portion ofthe data stream, with estimated glucose signal values. It is noted that“signal estimation” as used herein, is a broad term, which includesfiltering, data smoothing, augmenting, projecting, and/or otheralgorithmic methods that estimate glucose signal values based on presentand historical data.

It is noted that a glucose sensor can contain a processor, and/or thelike, that processes periodically received raw sensor data (e.g., every30 seconds). Although a data point can be available constantly, forexample by use of an electrical integration system in a chemo-electricsensor, relatively frequent (e.g., every 30 seconds), or less frequentdata point (e.g., every 5 minutes), can be more than sufficient forpatient use. It is noted that according to the Nyquist Theory, a datapoint is required about every 10 minutes to accurately describephysiological change in glucose in humans. This represents the lowestuseful frequency of sampling. However, it should be recognized that itcan be desirable to sample more frequently than the Nyquist minimum, toprovide for sufficient data in the event that one or more data pointsare lost, for example. Additionally, more frequently sampled data (e.g.,30-second) can be used to smooth the less frequent data (e.g., 5-minute)that are transmitted. It is noted that in this example, during thecourse of a 5-minute period, 10 determinations are made at 30-secondintervals.

In some embodiments of Signal Artifacts Replacement, signal estimationcan be implemented in the sensor and transmitted to a receiver foradditional processing. In some embodiments of Signal ArtifactsReplacement, raw data can be sent from the sensor to a receiver forsignal estimation and additional processing therein. In some embodimentsof Signal Artifacts Replacement, signal estimation is performedinitially in the sensor, with additional signal estimation in thereceiver.

In some embodiments of Signal Artifacts Replacement, wherein the sensoris an implantable glucose sensor, signal estimation can be performed inthe sensor to ensure a continuous stream of data. In alternativeembodiments, data can be transmitted from the sensor to the receiver,and the estimation performed at the receiver; It is noted however thatthere can be a risk of transmit-loss in the radio transmission from thesensor to the receiver when the transmission is wireless. For example,in embodiments wherein a sensor is implemented in vivo, the raw sensorsignal can be more consistent within the sensor (in vivo) than the rawsignal transmitted to a source (e.g., receiver) outside the body (e.g.,if a patient were to take the receiver off to shower, communicationbetween the sensor and receiver can be lost and data smoothing in thereceiver would halt accordingly). Consequently, It is noted that amultiple point data loss in the filter can take for example, about 25 toabout 40 minutes for the data to recover to near where it would havebeen had there been no data loss.

In some embodiments of Signal Artifacts Replacement, signal estimationis initiated only after signal artifacts are positively detected andstopped once signal artifacts are negligibly detected. In somealternative embodiments signal estimation is initiated after signalartifacts are positively detected and then stopped after a predeterminedtime period. In some alternative embodiments, signal estimation can becontinuously or continually performed. In some alternative embodiments,one or more forms of signal estimation can be accomplished based on theseverity of the signal artifacts, such as described in more detail withreference to U.S. Patent Publication Nos. 2005/0043598, 2007/0032706,2007/0016381, and 2007/0066873, and co-pending U.S. patent applicationSer. No. 11/762,638, filed on Jun. 13, 2007 and entitled “SYSTEMS ANDMETHODS FOR REPLACING SIGNAL ARTIFACTS IN A GLUCOSE SENSOR DATA STREAM,”all of which are incorporated herein by reference in their entirety.

In some embodiments of Signal Artifacts Replacement, the processormodule performs a linear regression. In one such implementation, theprocessor module performs a linear regression analysis of the n (e.g.,10) most recent sampled sensor values to smooth out the noise. A linearregression averages over a number of points in the time course and thusreduces the influence of wide excursions of any point from theregression line. Linear regression defines a slope and intercept, whichis used to generate a “Projected Glucose Value,” which can be used toreplace sensor data. This regression can be continually performed on thedata stream or continually performed only during the transient signalartifacts. In some alternative embodiments, signal estimation caninclude non-linear regression.

In another embodiment of Signal Artifacts Replacement, the processormodule performs a trimmed regression, which is a linear regression of atrimmed mean (e.g., after rejecting wide excursions of any point fromthe regression line). In this embodiment, after the sensor recordsglucose measurements at a predetermined sampling rate (e.g., every 30seconds), the sensor calculates a trimmed mean (e.g., removes highestand lowest measurements from a data set and then regresses the remainingmeasurements to estimate the glucose value.

In another embodiment of Signal Artifacts Replacement, the processormodule runs a non-recursive filter, such as a finite impulse response(FIR) filter. A FIR filter is a digital signal filter, in which everysample of output is the weighted sum of past and current samples ofinput, using only some finite number of past samples.

In another embodiment of Signal Artifacts Replacement, the processormodule runs a recursive filter, such as an infinite impulse response(IIR) filter. An IIR filter is a type of digital signal filter, in whichevery sample of output is the weighted sum of past and current samplesof input. In one exemplary implementation of an IIR filter, the outputis computed using 6 additions/subtractions and 7 multiplications asshown in the following equation:

${y(n)} = \frac{\begin{matrix}{{a_{0}*{x(n)}} + {a_{1}*{x\left( {n - 1} \right)}} + {a_{2}*{x\left( {n - 2} \right)}} + {a_{3}*}} \\{{x\left( {n - 3} \right)} - {b_{1}*{y\left( {n - 1} \right)}} - {b_{2}*{y\left( {n - 2} \right)}} - {b_{3}*{y\left( {n - 3} \right)}}}\end{matrix}}{b_{0}}$This polynomial equation includes coefficients that are dependent onsample rate and frequency behavior of the filter. Frequency behaviorpasses low frequencies up to cycle lengths of 40 minutes, and is basedon a 30 second sample rate. In alternative implementations, the samplerate and cycle lengths can be more or less. See Lynn “Recursive DigitalFilters for Biological Signals” Med. & Biol. Engineering, Vol. 9, pp.37-43, which is incorporated herein by reference in its entirety.

In another embodiment of Signal Artifacts Replacement, the processormodule runs a maximum-average (max-average) filtering algorithm. Themax-average algorithm smoothes data based on the discovery that thesubstantial majority of signal artifacts observed after implantation ofglucose sensors in humans, for example, is not distributed evenly aboveand below the actual blood glucose levels. It has been observed thatmany data sets are actually characterized by extended periods in whichthe noise appears to trend downwardly from maximum values withoccasional high spikes such as described in more detail above withreference to FIG. 7C, section 74b, which is likely in response tolimitations in the system that do not allow the glucose to fully reactat the enzyme layer and/or proper reduction of H₂O₂ at the counterelectrode, for example. To overcome these downward trending signalartifacts, the max-average calculation tracks with the highest sensorvalues, and discards the bulk of the lower values. Additionally, themax-average method is designed to reduce the contamination of the datawith non-physiologically high data from the high spikes.

In another embodiment of Signal Artifacts Replacement, the processormodule runs a “Cone of Possibility Replacement Method.” It is noted thatthis method can be performed in the sensor and/or in the receiver. TheCone of Possibility Detection Method utilizes physiological informationalong with glucose signal values in order define a “cone” ofphysiologically feasible glucose signal values within a human.Particularly, physiological information depends upon the physiologicalparameters obtained from continuous studies in the literature as well asour own observations. A first physiological parameter uses a maximalsustained rate of change of glucose in humans (e.g., about 4 to 5mg/dl/min) and a maximum sustained acceleration of that rate of change(e.g., about 0.1 to 0.2 mg/min/min). A second physiological parameteruses the knowledge that rate of change of glucose is lowest at themaxima and minima, which are the area of greatest risk in patienttreatment, such as described with reference to Cone of PossibilityDetection, above. A third physiological parameter uses the fact that thebest solution for the shape of the curve at any point along the curveover a certain time period (e.g., about 20-25 minutes) is a straightline. It is noted that the maximum rate of change can be narrowed insome instances. Therefore, additional physiological data can be used tomodify the limits imposed upon the Cone of Possibility ReplacementMethod for sensor glucose values. For example, the maximum per minuterate change can be lower when the subject is lying down or sleeping; onthe other hand, the maximum per minute rate change can be higher whenthe subject is exercising, for example.

The Cone of Possibility Replacement Method utilizes physiologicalinformation along with blood glucose data in order to improve theestimation of blood glucose values within a human in an embodiment ofSignal Artifacts Replacement. The Cone of Possibility Replacement Methodcan be performed on raw data in the sensor, on raw data in the receiver,or on smoothed data (e.g., data that has been replaced/estimated in thesensor or receiver by one of the methods described above) in thereceiver.

In other embodiments of Signal Artifacts Replacement, predictionalgorithms, also referred to as projection algorithms, can be used toreplace glucose data signals for data which does not exist because 1) ithas been discarded, 2) it is missing due to signal transmission errorsand the like, or 3) it represents a time period (e.g., future) for whicha data stream has not yet been obtained based on historic and/or presentdata. Prediction/projection algorithms include any of the abovedescribed Signal Artifacts Replacement algorithms, and differ only inthe fact that they are implemented to replace time points/periods duringwhich no data is available (e.g., for the above-described reasons),rather than including that existing data, within the algorithmiccomputation.

In some embodiments, signal replacement/estimation algorithms are usedto predict where the glucose signal should be, and if the actual datastream varies beyond a certain threshold of that projected value, thensignal artifacts are detected. In alternative embodiments, other dataprocessing can be applied alone, or in combination with theabove-described methods, to replace data signals during system noiseand/or signal artifacts.

FIG. 11 is a flow chart 1100 that illustrates the process of detectingand processing signal artifacts in some embodiments.

At block 1102, a sensor data receiving module, also referred to as thesensor data module, or processor module, receives sensor data (e.g., adata stream), including one or more time-spaced sensor data points. Insome embodiments, the data stream is stored in the sensor for additionalprocessing; in some alternative embodiments, the sensor periodicallytransmits the data stream to the receiver, which can be in wired orwireless communication with the sensor. In some embodiments, raw and/orfiltered data is stored in the sensor and/or transmitted and stored inthe receiver, as described in more detail elsewhere herein.

At block 1104, a signal artifacts detection module, also referred to asthe signal artifacts detector, or signal reliability module, isprogrammed to detect transient non-glucose related signal artifacts inthe data stream. In some embodiments, the signal artifacts detector cancomprise an oxygen detector, a pH detector, a temperature detector,and/or a pressure/stress detector, for example, the signal artifactsdetector 228 in FIG. 2. In some embodiments, the signal artifactsdetector is located within the processor 214 (FIG. 2) and utilizesexisting components of the glucose sensor to detect signal artifacts,for example by pulsed amperometric detection, counter electrodemonitoring, reference electrode monitoring, and frequency contentmonitoring, which are described elsewhere herein. In yet otherembodiments, the data can be sent from the sensor to the receiver whichcomprises programming in the processor 406 (FIG. 4) that performsalgorithms to detect signal artifacts, for example such as describedwith reference to “Cone of Possibility Detection” method and/or bycomparing raw data vs. filtered data, both of which are described inmore detail elsewhere herein.

In some exemplary embodiments, the processor module in either the sensorelectronics and/or the receiver electronics evaluates an intermittent orcontinuous signal-to-noise measurement to determine aberrancy of sensordata responsive to a signal-to-noise ratio above a set threshold. Insome exemplary embodiments, signal residuals (e.g., by comparing raw andfiltered data) are intermittently or continuously analyzed for noiseabove a set threshold. In some exemplary embodiments, patternrecognition can be used to identify noise associated with physiologicalconditions, such as low oxygen, or other known signal aberrancies.Accordingly, in these embodiments, the system can be configured, inresponse to aberrancies in the data stream, to trigger signalestimation, adaptively filter the data stream according to theaberrancy, and the like, as described in more detail elsewhere herein.

In some embodiments, one or more signal residuals are obtained bycomparing received data with filtered data, whereby a signal artifactcan be determined. In some embodiments, a signal artifact event isdetermined to have occurred if the residual is greater than a threshold.In some exemplary embodiments, another signal artifact event isdetermined to have occurred if the residual is greater than a secondthreshold. In some exemplary embodiments, a signal artifact event isdetermined to have occurred if the residual is greater than a thresholdfor a period of time or an amount of data. In some exemplaryembodiments, a signal artifact event is determined to have occurred if apredetermined number of signal residuals above a predetermined thresholdoccur within a predetermined time period (or an amount of data). In someexemplary embodiments, an average of a plurality of residuals isevaluated over a period of time or amount of data to determine whether asignal artifact has occurred. The use of residuals for noise detectioncan be preferred in circumstances where data gaps (non-continuous) dataexists.

In some exemplary embodiments, a differential, also referred to as aderivative of the residual (delta residual), is determined by comparinga first residual (e.g., at a first time point) and a second residual(e.g., at a second time point), wherein a signal artifact event isdetermined to have occurred when the differential is above apredetermined threshold. In some exemplary embodiments, a signalartifact event is determined to have occurred if the differential isgreater than a threshold for a period of time or amount of data. In someexemplary embodiments, an average of a plurality of differentials iscalculated over a period of time or amount of data to determine whethera signal artifact has occurred.

Numerous embodiments for detecting signal artifacts are described inmore detail in the section entitled, “Signal Artifacts Detection,” allof which are encompassed by the signal artifacts detection at block1104.

At block 1106, the processor module is configured to process the sensordata based at least in part on whether the signal artifact event hasoccurred.

In some embodiments, the sensor data is filtered in the receiverprocessor to generate filtered data if the signal artifact event isdetermined to have occurred; filtering can be performed either on theraw data, or can be performed to further filter received filtered data,or both.

In some embodiments, signal artifacts detection and processing isutilized in outlier detection, such as described in more detailelsewhere herein, wherein a disagreement between time correspondingreference data and sensor data can be analyzed, e.g., noise analysisdata (e.g., signal artifacts detection and signal processing) can beused to determine which value is likely more reliable (e.g., whether thesensor data and/or reference data can be used for processing). In someexemplary embodiments wherein the processor module receives referencedata from a reference analyte monitor, a reliability of the receiveddata is determined based on signal artifacts detection (e.g., if asignal artifact event is determined to have occurred.) In some exemplaryembodiments, a reliability of the sensor data is determined based onsignal artifacts detection (e.g., if the signal artifact event isdetermined to have not occurred.) The term “reliability,” as usedherein, is a broad term and is used in its ordinary sense, including,without limitation, a level of confidence in the data (e.g., sensor orreference data), for example, a positive or negative reliance on thedata (e.g., for calibration, display, and the like) and/or a rating(e.g., of at least 60%, 70%, 80%, 90% or 100% confidence thereon.)

In some embodiments wherein a matched data pair is formed by matchingreference data to substantially time corresponding sensor data (e.g.,for calibration and/or outlier detection) described in more detailelsewhere herein, matching of a data pair can be configured to occurbased on signal artifacts detection (e.g., only if a signal artifactevent is determined to have not occurred.) In some embodiments whereinthe reference data is included in a calibration factor for use incalibration of the glucose sensor as described in more detail elsewhereherein, the reference data can be configured to be included based onsignal artifacts detection (e.g., only if the signal artifact event isdetermined to have not occurred.) In general, results of noise analysis(e.g., signal artifact detection and/or signal processing) can be usedto determine when to use or eliminate a matched pair for use incalibration (e.g., calibration set).

In some embodiments, a user is prompted for a reference glucose valuebased on signal artifacts detection (e.g., only if a signal artifactevent is determined to have not occurred.) While not wishing to be boundby theory, it is believed certain more preferable times for calibration(e.g., not during noise episodes) can be detected and processed byprompting the user for calibration during those times.

In some embodiments, results of noise analysis (e.g., signal artifactdetection and/or signal processing) can be used to determine how toprocess the sensor data. For example, different levels of signalprocessing and display (e.g., raw data, integrated data, filtered datautilizing a first filter, filtered data utilizing a second filter, whichmay be “more aggressive” than the first filter by filtering over alarger time period, and the like.) Accordingly, the different levels ofsignal processing and display can be selectively chosen responsive to areliability measurement, a positive or negative determination of signalartifact, and/or signal artifacts above first and second predeterminedthresholds.

In some embodiments, results of noise analysis (e.g., signal artifactdetection and/or signal processing) can be used to determine when toutilize and/or display different representations of the sensor data(e.g., raw vs. filtered data), when to turn filters on and/or off (e.g.,processing and/or display of certain smoothing algorithms), and/or whento further process the sensor data (e.g., filtering and/or displaying).In some embodiments, the display of the sensor data is dependent uponthe determination of signal artifact(s). For example, when a certainpredetermined threshold of signal artifacts have been detected (e.g.,noisy sensor data), the system is configured to modify or turn off aparticular display of the sensor data (e.g., display filtered data,display processed data, disable display of sensor data, display range ofpossible data values, display indication of direction of glucose trenddata, replace sensor data with predicted/estimated sensor data, and/ordisplay confidence interval representative of a level of confidence inthe sensor data.) In some exemplary embodiments, a graphicalrepresentation of filtered sensor data is displayed if the signalartifact event is determined to have occurred. Alternatively, when acertain predetermined threshold of signal artifacts has not beendetected (e.g., minimal, insignificant, or no noise in the data signal),the system is configured to modify or turn on a particular display ofthe sensor data (e.g., display unfiltered (e.g., raw or integrated)data, a single data value, an indication of direction of glucose trenddata, predicted glucose data for a future time period and/or aconfidence interval representative of a level of confidence in thesensor data.)

In some embodiments wherein a residual (or differential) is determinedas described in more detail elsewhere herein, the residual (ordifferential) is used to modify the filtered data during signal artifactevent(s). In one such exemplary embodiment, the residual is measured andthen added back into the filtered signal. While not wishing to be boundby theory, it is believed that some smoothing algorithms may result insome loss of dynamic behavior representative of the glucoseconcentration, which disadvantage may be reduced or eliminated by theadding of the residual back into the filtered signal in somecircumstances.

In some embodiments, the sensor data can be modified to compensate for atime lag, for example by predicting or estimating an actual glucoseconcentration for a time period considering a time lag associated withdiffusion of the glucose through the membrane, digital signalprocessing, and/or algorithmically induced time lag, for example.

FIG. 12 is a graph that illustrates a raw data stream from a glucosesensor for approximately 24 hours with a filtered version of the samedata stream superimposed on the same graph. Additionally, this graphillustrates a noise episode, the beginning and end of which was detectedby a noise detection algorithm of the preferred embodiments, and duringwhich a particular filter was applied to the data. The x-axis representstime in minutes; the y-axis represents the raw and filtered data valuesin counts. In this example, the raw data stream was obtained in 5 minuteintervals from a transcutaneous glucose sensor such as described in moredetail above, with reference to FIG. 1B and in U.S. Patent PublicationNo. US-2006-00201087-A1.

In section 1202 of the data, which encompasses an approximately 14 hourperiod up to time=2:22, the filtered data was obtained by applying a3-point moving average window to the raw data. During that period, thenoise detection algorithm was applied to detect a noise episode. In thisexample, the algorithm included the following: calculating a residualsignal by subtracting the filtered data from the raw data (e.g., foreach 5-minute point); calculating a differential by subtracting theresidual for each 5-minute point from its previous 5-minute residual;determining if each differential exceeds a threshold of 5000 counts (anddeclaring a noisy point if so); and determining whether 6 out of 12points in the past 1 hour exceed that threshold (and declaring a noiseepisode if so). Accordingly, a noise episode was declared at time=2:22and a more aggressive filter was applied as described with reference tosection 1204.

In section 1204 of the data, also referred to as a noise episode, whichencompasses an approximately 5½ hour period up to time=7:57, thefiltered data was obtained by applying a 7-point moving average windowto the raw data. The 7-point moving average window was in this examplewas an effective filter in smoothing out the noise in the data signal ascan be seen on the graph. During that period, an algorithm was appliedto detect when the noise episode had ended. In this example, thealgorithm included the following: calculating a residual signal bysubtracting the filtered data (using the 3-point moving average filterdescribed above) from the raw data (e.g., for each 5-minute point);calculating a differential of the residual by subtracting the residualfor each 5-minute point from its previous 5-minute residual; determiningif each differential exceeds a threshold of 5000 counts (and declaring anoisy point if so); and determining whether less than 2 noisy points hadoccurred in the past hour (and declaring the noise episode over if so).Accordingly, the noise episode was declared as over at time-7:57 and theless aggressive filter (e.g., 3-point moving average) was again appliedwith the noise detection algorithm as described with reference tosection 1202, above.

In section 1206 of the data, which encompasses more than 4 hours ofdata, the filtered data was obtained by applying a 3-point movingaverage window to the raw data. During that period, the noise detectionalgorithm (described above) did not detect a noise episode. Accordingly,raw or minimally filtered data could be displayed to the patient duringthis time period.

It was shown that the above-described example provided smoother glucoseinformation during noise episodes, by applying a more aggressive filterto smooth out the noise. It is believed that when displayed, thesmoother data will avoid presenting potentially misleading or inaccurateinformation to the user. Additionally, it was shown in theabove-described example that during non-noisy periods (when noiseepisodes are not detected), raw or less aggressively filtered data canbe displayed to the user in order to provide more accurate data withminimal or no associated filter-induced time lag in the data.Furthermore, it is believed that proper detection of noise episodes aidsin determining proper times for calibration, ensuring more accuratecalibration than may otherwise be possible.

In the above-described example, the criteria for the onset & offset ofnoise episodes were different; for example, the onset criteria included6 out of 12 points in the past 1 hour exceeding a threshold, while theoffset criteria included less than 2 noisy points in the past 1 hour. Inthis example, these different criteria were found to create smoothertransitions in the data between the raw and filtered data and avoidedfalse detections of noise episodes.

FIG. 13 is a flowchart 1300 that illustrates a process for determining arate of change of a continuous analyte sensor signal, in one embodiment.

At block 1302, a sensor data receiving module, also referred to as thesensor data module, computer system, or processor module, receivessensor data (e.g., a data stream), including one or more time-spacedsensor data points hereinafter referred to as “data stream,” “sensordata,” “sensor analyte data”, “signal,” from a sensor via the receiver,which can be in wired or wireless communication with the sensor. Thesensor data receiving module is described in more detail elsewhereherein, for example, with reference to FIG. 5.

At block 1304, optionally determining a level of noise on the sensorsignal, which is described in more detail elsewhere herein.

At block 1306, the computer system (e.g., processor module) calculates arate of change for a window of sensor data, wherein the window of sensordata includes two or more sensor data points. In some embodiments, thewindow of sensor data is a user selectable time period. In someembodiments, the window of sensor data is a programmable time period. Insome embodiments, wherein the window of sensor data adaptively adjustsbased at least in part on a level of noise in the sensor data.Accordingly, one or more windows of data can be user-selected (oradaptively-selected by the computer system) depending upon what type oftrend data is to be displayed. As one example of a window of data, a“current trend” includes rate of change information from recent data(e.g., within about 5, 10, 15, 20, 25, 30, 35, 40 minutes). As anotherexample of a window of data, a “sustained trend” includes rate of changeinformation from a wider window of data than the current trend (e.g.,within about 20, 30, 40, 50, 60 or more minutes).

In some embodiments, the computer system is configured to use either rawsensor data or filtered sensor data (including adaptive filtering) inthe rate of change calculation depending at least in part upon the levelof noise determined. In some embodiments, the rate of change calculationcomprises calculating at least two rate of change calculations, andfiltering the rate of change calculation to obtain a filtered rate ofchange value as described in more detail elsewhere herein. In someembodiments, the rate of change calculation comprises calculating atleast two point-to-point rate of change calculations, and wherein therate of change calculation further comprises adaptively selecting afilter to apply to the point-to-point rate of change calculation basedat least in part on the level of noise determined.

In some embodiments, the rate of change calculation described herein isused to predict one or more analyte values, which is described in moredetail with reference to FIG. 14, for example. In some embodiments, atrend arrow is displayed on the user interface based at least in part onthe rate of change calculation described herein. In some embodiments,the rate of change calculation described herein is issued to determine atherapy instruction, for example, a medicament delivery type and/oramount of medicament for delivery via an open-loop, semi-open loopand/or closed loop system.

FIG. 14 is a flowchart 1400 that illustrates a process for predictionbased on a continuous analyte sensor signal, in one embodiment,including determining whether to trigger a predicted hypoglycemia orpredicted hyperglycemia alarm and/or to display a predicted time topredicted hypoglycemia or predicted hyperglycemia. In the embodimentdescribed herein with reference to FIG. 13, a “free flow algorithm” isused for the predictive algorithm, which is in contrast to aconventional predictive algorithms that use model or curve-fitting typealgorithms. Advantageously, the free flow algorithm described herein isrobust to the non-stationary condition of signal.

At block 1402, a sensor data receiving module, also referred to as thesensor data module, computer system, or processor module, receivessensor data (e.g., a data stream), including one or more time-spacedsensor data points hereinafter referred to as “data stream,” “sensordata,” “sensor analyte data”, “signal,” from a sensor via the receiver,which can be in wired or wireless communication with the sensor. Thesensor data receiving module is described in more detail elsewhereherein, for example, with reference to FIG. 5.

At block 1404, the computer system optionally filters the sensor data toobtain an estimated sensor value (e.g., calibrated glucose concentrationbased on sensor data). In some embodiments, the estimated sensor valueis at a time t=0; for example, the computer system compensates for timelag associated with filtering and/or the sensor. However, the computersystem can additionally and/or alternatively compensate for a time lagin other processing step or modules, such as block 1410.

At block 1406, the computer system calculates a rate of change based ona time series analysis of rate of change information, wherein the timeseries analysis includes at least two rate of change values. In someembodiments, the rate of change values can be obtained by gradienttracking, multiple rate of change calculations, point-to-point rate ofchange calculations, and/or the like. In one exemplary embodiment, thecomputer system calculates at least two point-to-point rate of changevalues, as described in more detail elsewhere herein.

At block 1408, the computer system filters the at least two rate ofchange values to obtain a filtered ROC value. In some embodiments, thecomputer system continuously filters the at least two point-to-pointrate of change values to obtain a filtered ROC value.

At block 1410, the computer system determines whether to trigger apredicted hypoglycemia or predicted hyperglycemia alarm and/or display apredicted time to predicted hypoglycemia or predicted hyperglycemia. Forexample, in some embodiments, the computer system determines a predictedvalue for a future time period based on the estimated sensor value, thefiltered ROC value and a time to the future time period, which can beprogrammed into the computer or user selectable (e.g., 5, 10, 15, 20minutes or more). In some embodiments, the computer system compares thepredicted value against a threshold (e.g., 50, 60, 70, 80, 90 or 100mg/dL for predicted hypoglycemia and/or 160, 180, 200, 220 or 240 forpredicted hyperglycemia), which can be programmed into the computersystem or user selectable. In some embodiments, the computer systemtriggers an alarm when the predicted value passes the threshold.

In some embodiments, the computer system determines a predicted time toa threshold, wherein the predicted time is based at least in part on theestimated sensor value, the filtered ROC value and a threshold (e.g.,50, 60, 70, 80, 90 or 100 mg/dL for predicted hypoglycemia and/or 160,180, 200, 220 or 240 for predicted hyperglycemia), which can beprogrammed into the computer system or user selectable. In someembodiments, the computer system is configured to display the predictedtime to threshold on a user interface. In some embodiments, the computersystem is configured to display the predicted time to threshold onlywhen the predicted time is below a predetermined value.

In some embodiments, the computer system determines an insulin therapybased at least in part on the filtered ROC value. In some embodiments,the computer system displays a trend arrow on a user interface based atleast in part on the filtered ROC value.

In some embodiments, a trend arrow is displayed on the user interfacebased at least in part on the filtered rate of change calculationdescribed herein. In some embodiments, the filtered rate of changecalculation described herein is issued to determine a therapyinstruction, for example, a medicament delivery type and/or amount fordelivery via an open-loop, semi-open loop and/or closed loop system.

Reference is now made to FIG. 15, which is a flow chart 1500 thatillustrates the process of receiving sensor data, setting a mode andfurther processing data based upon the mode. In general, the modes ofthe preferred embodiments enable systems and methods associated withprocessing analyte data, alarms, medicament delivery, at the like, to beadapted to and/or customized for a user's mode (e.g., activity,physiological condition and/or preference). In one embodiment, havingdifferent modes allows the system to evaluate and/or process the analytedata, (e.g., concentration, trends, etc) using additional informationregarding the user's activity and/or preference (or “mode”). Forexample, when a person is exercising, his/her glucose levels mayincrease or decrease in trends that would be abnormal under any othercircumstances; by setting the appropriate mode, the system is configuredto modify its processing associated with the user in a particular mode,e.g., “exercise mode” to provide alarms, analyte estimates, trendinformation, therapy recommendations, and the like, customized with oneor more criteria associated with exercise.

In some preferred embodiments, systems and methods are provided toaccount for the various events that can occur in the life of the userand/or the preferences (e.g. the user simply not wanting to bedisturbed) of the user to determine whether alarms and/or medicamentdelivery instructions are necessary in response to the user's glucosedata. Depending upon the event that the user has scheduled or the user'spreference, the user may or may not want to be alarmed of certainanalyte values and/or trends in their glucose levels and therefore, bysetting a mode, the user can control a sensitivity of the alarms (e.g.,high, medium, low) to dictate how often the user is alarmed. Forexample, when a user is sleeping he/she may not want to be alarmed oflevels and/or changes in glucose unless they are of urgent need (e.g.low sensitivity), accordingly, the systems is configured to alter alarmcriteria to be less sensitive during “resting mode.”

At block 1510, a sensor data receiving module, also referred to as thesensor data module, or processor module, receives sensor data (e.g., adata stream), including one or more time-spaced sensor data pointshereinafter referred to as “data stream,” “sensor data,” “sensor analytedata”, “signal,” from a sensor via the receiver, which can be in wiredor wireless communication with the sensor. The sensor data receivingmodule is described in more detail elsewhere herein, for example, withreference to FIG. 5.

At block 1520, a mode setting module, sets the mode of the system. Inpreferred embodiments, the mode is set based at least in part upon oneor more inputs (e.g. buttons, menus) and/or data received from variousdevices (e.g. accelerometer, temperature sensor, timer, mode profile,scheduling software). In the preferred embodiment, the system, at leastin part, uses the data received from inputs and/or devices to set a modefrom a plurality of predetermined modes (e.g. resting mode, do notdisturb mode, exercise mode, illness mode, menstruation mode, mealtimemode, snooze mode, day mode, night mode, hyperglycemia mode,hypoglycemia mode, clinical risk mode, noise mode, and the like). Ingeneral, each mode correlates to an activity, event, physiologicalcondition, sensor condition, and/or preference of the user.

In some embodiments, the system is configured to set the mode at leastin part responsive to receipt of a user input. In an exemplaryembodiment, the system comprises one or more buttons, and wherein theprocessor module is configured to receive the user input by selection ofone or more buttons (e.g., dedicated mode buttons or universal buttonsthat enable selection from a user interface). In another exemplaryembodiment, the system comprises a screen configured to display one ormore menus and receive the user input by selection of one or more itemsfrom the one or more menus. In some embodiments, the system isconfigured to operably connect (e.g. via wired connection, wirelessconnection) with another computer system (e.g. mobile phone, personaldigital assistant, personal computer, and the like) such that data (e.g.modes, mode profiles) can be transmitted to the system of the preferredembodiments. In an exemplary embodiment, the system is operablyconnected using a wired connection (e.g. cat 5, USB). In yet anotherexemplary embodiment, the system is operably connected using a wirelessconnection. Advantageously, setting of modes as described in thepreferred embodiments enables the user to switch preferences and/orcriteria associated with alarms, therapy instruction, data processing,and/or the like, to correspond with the user's life quickly and easily.

In some embodiments, the system is configured to set a mode responsiveto programming configured to schedule and organize events on a calendar(e.g. Microsoft Outlook, Eudora). In another embodiment, the system isfurther configured to set the mode at least in part responsive to a modeprofile, wherein the system or the user can set the mode profile. Forexample, a “work week” mode profile would have defined modes thatcorrespond to the user's usual schedule during a 5-day work week. Modeprofiles can be default system profiles, customizable default systemprofiles, user definable profiles, and the like. Accordingly, theembodiments described herein allow the user to schedule a series oftime-based modes that occur on a recurring basis.

In some embodiments, the system is configured to automatically set themode at least in part responsive to a comparison of data with one ormore criteria (e.g. accelerometer, temperature sensor and/or criteriaassociated with the adaptive mode learning module as described in moredetail herein). In an exemplary embodiment, the system includes and/oris configured to receive data from an accelerometer. The term“accelerometer” as used herein is a broad term and is to be given itsordinary and customary meaning to a person of ordinary skill in the art(and is not limited to a special or customized meaning), and furthermorerefers without limitation to a device that monitors movement. In anotherexemplary embodiment, the system includes and/or is configured toreceive data from a temperature sensor.

In one exemplary embodiment, the system comprises programming configuredto automatically set the mode at least in part responsive to an adaptivemode-learning module (e.g. within the processor module). Preferably, theadaptive mode-learning module is configured to process (historic) sensordata and time-corresponding modes over time and determine patterns ortrends used to automatically set a mode when similar trends or patternsare detected on real-time sensor data. For example, the system isconfigured to adaptively switch in and out of modes without constantuser interaction by comparing real time data with historic data.

In yet another exemplary embodiment, the system comprises a timerassociated with one or more modes, wherein the timer is configured toset the mode for a predetermined amount of time (e.g. 20 minutes, 30minutes, 1 hour, 4 hours, 8 hours, etc.). Either the user or the systemcan set the timer. In some embodiments, the timer is a default timerthat allows a user to select a mode with a single click for apredetermined time period. In general, the system is configured to haveany combination of automatic timers, default timers, user settabletimes, profile driven timers, and/or the like, for any combination ofone or more modes.

In one embodiment, the system is configured to classify a level of noisein the continuous analyte sensor data, as described in more detailelsewhere herein. The level of noise can be an indicator of a level ofaccuracy of the sensor data and can be user to set a mode (e.g.automatically by the system) responsive at least in part to the level ofnoise.

At block 1530, continuous sensor data is received and processed based atleast in part on the mode. In general, each mode is associated with oneor more criteria and/or inputs (e.g. alarm criteria and/or types ofalarms, parameters associated with calculating a therapy instruction,and/or processing instructions associated with estimating analyte valuesand/or outputting analyte sensor information). In general, modes providecustomized processing of sensor data associated with an activity, event,physiological condition, sensor condition, and/or preference of a user,for example.

In some embodiments, the system is configured to determine a type oralarm to activate (e.g. audible sound, visual display, vibration,alphanumeric message, and/or wireless transmission) based at least inpart on the mode. In some embodiments, the system is configured to alterthe alarm criteria (e.g., threshold analyte values, rate of change,and/or acceleration).

In some embodiments, the system comprises a therapy module (e.g.processor module) configured to determine a therapy instruction (e.g. aquantified dosage of a medicament, an activity, a recommend caloricintake) based at least in part on the mode. In some embodiments, thesystem is operably connected with a medicament delivery device (e.g. adevice used to deliver a dose of insulin to the user), wherein themedicament type and/or amount to be delivered is based at least in parton the mode. In some embodiments, the system is configured to require avalidation of the medicament delivery instruction prior to delivery ofthe medicament (e.g. insulin) based at least in part on the mode.Advantageously, the therapy instructions of the preferred embodiment arecustomizable for an activity, event, physiological condition, sensorcondition, and/or preference of a user.

In some embodiments, the estimation of analyte values is based at leastin part on a mode. In one such exemplary embodiment, the system isconfigured to aggressively filter data during “night mode” because thetrade off of a time delay associated with aggressively filtered dataversus reduced false alarms caused by noise spikes in unfiltered datawould be advantageous to a sleeping patient. In some embodiments, theuser interface is controlled, at least in part based on the mode.

As one example, resting mode sets a reduced sensitivity of alarms (e.g.,hypoglycemic alarms with analyte thresholds that are 5%, 10%, 15%, 20%or more higher than default settings) and/or to turn off audible alarmssounds. As another example, do not disturb mode is activated by a buttonon the receiver's user interface, whereby a user can simply press the“do not disturb button” and all alarms and therapy calculations turn offand/or are not activated for a predetermined time period. As anotherexample, exercise mode sets parameters to ensure the therapy modulecalculates appropriate caloric intake suitable during and afterexercise. As another example, mealtime mode sets parameters to ensurethe therapy module calculates appropriate medicament delivery suitableduring and after a meal. As another example, day mode is associated withmore sensitive alarm thresholds and more noticeable alarm types.

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Methods and devices that are suitable for use in conjunction withaspects of the preferred embodiments are disclosed in U.S. patentapplication Ser. No. 09/447,227 filed Nov. 22, 1999 and entitled “DEVICEAND METHOD FOR DETERMINING ANALYTE LEVELS”; U.S. patent application Ser.No. 11/654,135 filed Jan. 17, 2007 and entitled “POROUS MEMBRANES FORUSE WITH IMPLANTABLE DEVICES”; U.S. patent application Ser. No.11/654,140 filed Jan. 17, 2007 and entitled “MEMBRANES FOR AN ANALYTESENSOR”; U.S. patent application Ser. No. 11/691,426 filed Mar. 26, 2007and entitled “ANALYTE SENSOR”; U.S. patent application Ser. No.12/103,594 filed Apr. 15, 2008 and entitled “BIOINTERFACE WITH MACRO-AND MICRO-ARCHITECTURE”; U.S. patent application Ser. No. 12/113,724filed May 1, 2008 and entitled “LOW OXYGEN IN VIVO ANALYTE SENSOR”; U.S.patent application Ser. No. 12/055,098 filed Mar. 25, 2008 and entitled“ANALYTE SENSOR”; U.S. patent application Ser. No. 12/054,953 filed Mar.25, 2008 and entitled “ANALYTE SENSOR”; U.S. patent application Ser. No.12/055,114 filed Mar. 25, 2008 and entitled “ANALYTE SENSOR”; U.S.patent application Ser. No. 12/133,820 filed Jun. 5, 2008 and entitled“INTEGRATED MEDICAMENT DELIVERY DEVICE FOR USE WITH CONTINUOUS ANALYTESENSOR”; U.S. patent application Ser. No. 12/133,789 filed Jun. 5, 2008and entitled “INTEGRATED MEDICAMENT DELIVERY DEVICE FOR USE WITHCONTINUOUS ANALYTE SENSOR”; U.S. patent application Ser. No. 12/133,761filed Jun. 5, 2008 and entitled “INTEGRATED MEDICAMENT DELIVERY DEVICEFOR USE WITH CONTINUOUS ANALYTE SENSOR”; U.S. patent application Ser.No. 12/133,738 filed Jun. 5, 2008 and entitled “INTEGRATED MEDICAMENTDELIVERY DEVICE FOR USE WITH CONTINUOUS ANALYTE SENSOR”; U.S. patentapplication Ser. No. 12/139,305 filed Jun. 13, 2008 and entitled“ELECTRODE SYSTEMS FOR ELECTROCHEMICAL SENSORS”; U.S. patent applicationSer. No. 12/175,391 filed Jul. 17, 2008 and entitled “TRANSCUTANEOUSANALYTE SENSOR”; U.S. patent application Ser. No. 12/182,008 filed Jul.29, 2008 and entitled “INTEGRATED RECEIVER FOR CONTINUOUS ANALYTESENSOR”; U.S. patent application Ser. No. 12/182,073 filed Jul. 29, 2008and entitled “INTEGRATED RECEIVER FOR CONTINUOUS ANALYTE SENSOR”; U.S.patent application Ser. No. 12/182,083 filed Jul. 29, 2008 and entitled“INTEGRATED RECEIVER FOR CONTINUOUS ANALYTE SENSOR”; U.S. patentapplication Ser. No. 12/195,191 filed Aug. 20, 2008 and entitled “SYSTEMAND METHODS FOR PROCESSING ANALYTE SENSOR DATA”; U.S. patent applicationSer. No. 12/195,773 filed Aug. 21, 2008 and entitled “SYSTEM AND METHODSFOR PROCESSING ANALYTE SENSOR DATA”; U.S. patent application Ser. No.12/247,137 filed Oct. 7, 2008 and entitled “IMPLANTABLE ANALYTE SENSOR”;U.S. patent application Ser. No. 12/250,918 filed Oct. 14, 2008 andentitled “ANALYTE SENSOR”; U.S. patent application Ser. No. 12/253,125filed Oct. 16, 2008 and entitled “SIGNAL PROCESSING FOR CONTINUOUSANALYTE SENSOR”; U.S. patent application Ser. No. 12/253,120 filed Oct.16, 2008 and entitled “SIGNAL PROCESSING FOR CONTINUOUS ANALYTE SENSOR”;U.S. patent application Ser. No. 12/253,064 filed Oct. 16, 2008 andentitled “SIGNAL PROCESSING FOR CONTINUOUS ANALYTE SENSOR”; U.S. patentapplication Ser. No. 12/252,996 filed Oct. 16, 2008 and entitled “SIGNALPROCESSING FOR CONTINUOUS ANALYTE SENSOR”; U.S. patent application Ser.No. 12/252,967 filed Oct. 16, 2008 and entitled “SIGNAL PROCESSING FORCONTINUOUS ANALYTE SENSOR”; and U.S. patent application Ser. No.12/252,952 filed Oct. 16, 2008 and entitled “SIGNAL PROCESSING FORCONTINUOUS ANALYTE SENSOR”.

All references cited herein, including but not limited to published andunpublished applications, patents, and literature references, areincorporated herein by reference in their entirety and are hereby made apart of this specification. To the extent publications and patents orpatent applications incorporated by reference contradict the disclosurecontained in the specification, the specification is intended tosupersede and/or take precedence over any such contradictory material.

The term “comprising” as used herein is synonymous with “including,”“containing,” or “characterized by,” and is inclusive or open-ended anddoes not exclude additional, unrecited elements or method steps.

All numbers expressing quantities of ingredients, reaction conditions,and so forth used in the specification are to be understood as beingmodified in all instances by the term “about.” Accordingly, unlessindicated to the contrary, the numerical parameters set forth herein areapproximations that may vary depending upon the desired propertiessought to be obtained. At the very least, and not as an attempt to limitthe application of the doctrine of equivalents to the scope of anyclaims in any application claiming priority to the present application,each numerical parameter should be construed in light of the number ofsignificant digits and ordinary rounding approaches.

The above description discloses several methods and materials of thepresent invention. This invention is susceptible to modifications in themethods and materials, as well as alterations in the fabrication methodsand equipment. Such modifications will become apparent to those skilledin the art from a consideration of this disclosure or practice of theinvention disclosed herein. Consequently, it is not intended that thisinvention be limited to the specific embodiments disclosed herein, butthat it cover all modifications and alternatives coming within the truescope and spirit of the invention.

1. A method for determining a rate of change of a continuous glucosesensor signal, comprising: receiving sensor data from a continuousanalyte sensor, the sensor data comprising one or more sensor datapoints; determining, using a processor module, a level of noise in thesensor data; and calculating, using the processor module, a rate ofchange for a window of time of sensor data, wherein the window of timeof sensor data comprises two or more sensor data points and wherein theprocessor module adaptively adjusts the length of the window of sensordata based at least in part on the determined level of noise in thesensor data.
 2. A system for determining a rate of change of acontinuous glucose sensor signal, comprising: a continuous glucosesensor configured to generate sensor data associated with a glucoseconcentration in a host; and a computer system configured to determine alevel of noise in the sensor data and calculate a rate of change for awindow of sensor data, the sensor data comprising two or more sensordata points, wherein the computer system is configured to adaptivelyadjust the length of the window of sensor data based at least in part onthe determined level of noise in the sensor data.
 3. A method fordetermining a rate of change of a continuous glucose sensor signal,comprising: receiving sensor data from a continuous analyte sensor;determining a level of noise in the sensor data; and calculating, usinga processor module, a rate of change for a window of the sensor data,wherein the window of sensor data comprises two or more sensor datapoints, wherein the step of calculating a rate of change comprises atleast two rate of change calculations, and wherein the step ofcalculating a rate of change further comprises adaptively selecting afilter to apply to the at least two rate of change calculations based atleast in part on the level of noise determined.
 4. A system fordetermining a rate of change of a continuous glucose sensor signal,comprising: a continuous glucose sensor configured to generate sensordata associated with a glucose concentration in a host; and a computersystem configured to determine a level of noise in the sensor data andcalculate a rate of change for a window of the sensor data, wherein thewindow of sensor data comprises two or more sensor data points, whereinthe rate of change calculation comprises calculating at least two rateof change calculations, and wherein the rate of change calculationfurther comprises adaptively selecting a filter to apply to the rate ofchange calculation based at least in part on the level of noisedetermined.